Detecting, assessing and managing epilepsy using a multi-variate, metric-based classification analysis

ABSTRACT

A method for identifying changes in an epilepsy patient&#39;s disease state, comprising: receiving at least one body data stream; determining at least one body index from the at least one body data stream; detecting a plurality of seizure events from the at least one body index; determining at least one seizure metric value for each seizure event; performing a first classification analysis of the plurality of seizure events from the at least one seizure metric value; detecting at least one additional seizure event from the at least one determined index; determining at least one seizure metric value for each additional seizure event, performing a second classification analysis of the plurality of seizure events and the at least one additional seizure event based upon the at least one seizure metric value; comparing the results of the first classification analysis and the second classification analysis; and performing a further action.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation-in-Part of U.S. patentapplication Ser. Nos. 13/091,033, filed Apr. 20, 2011, and 13/333,235,filed Dec. 21, 2011, both pending and both Continuations-in-Part of U.S.patent application Ser. No. 13/040,996, filed Mar. 4, 2011, now pending.The present application is also a Continuation-in-Part of U.S. patentapplication Ser. No. 13/288,886, filed Nov. 3, 2011, which is aContinuation-in-Part of U.S. patent application Ser. No. 13/098,262,filed Apr. 29, 2011, which is a Continuation-in-Part of U.S. patentapplication Ser. No. 12/896,525, filed Oct. 1, 2010. Each of U.S. patentapplication Ser. Nos. 13/091,033, 13/333,235, 13/040,996, 13/288,886,13/098,262, and 12/896,525 are hereby incorporated by reference in theirentirety.

BACKGROUND

1. Field of the Invention

This invention relates generally to medical device systems and, moreparticularly, to medical device systems and methods capable of assessingand managing epileptic events related to epilepsy based upon seizureclassification.

2. Description of the Related Art

Generalized tonic-clonic status epilepticus, referred to herein asConvulsive Status Epilepticus (CSE), is a neurological emergency with anestimated incidence of about 20 out of 100,000 patients. CSE is alsoassociated with a mortality rate between 3% and 40% depending onetiology, age, status type, and status duration and is considered inthis disclosure an extreme event. CSE, in particular, requiresimmediate, aggressive, and effective treatment to stop seizure activity,to prevent neuronal damage, systemic complications, and the possibilityof death. Most investigations on prognosis of status epilepticus (SE)have focused on mortality. Some research suggests that SE outcomebasically depends on the etiological and biological background of the SEepisode, and that the earlier the therapeutic intervention, the higherthe probability of controlling it. Additionally, non-convulsive statusepilepticus (nCSE), while not a medical emergency of the magnitude ofCSE, is also an extreme epileptic event because it increases the risk ofbodily injury and neurologic deficits such as permanent, potentiallysevere impairment of memory.

SE and CSE are defined based on the duration of a single seizure and itsvariations, or on the lack of recovery of certain neurologic functionsto their inter-ictal (baseline) levels in the context of closely spacedseizures. While it is common to focus on seizure duration or frequencyas measured from patient EEG, and whether the patient is conscious/awareor not, such a focus has important limitations, because signals orindices from other body systems (such as cardio-vascular, respiratory,endocrine, and metabolic) are adversely impacted by the seizures,undergoing extreme, life-threatening changes that do not receive thesame attention as the seizures. These cardio-respiratory, metabolic, andendocrine extreme changes may directly contribute to the morbidity andmortality associated with SE. In the present state of the art, SE isviewed and treated narrowly (and ineffectively) as mainly a braincondition. Current diagnostic tools do not facilitate earlydetection/anticipation of extreme epileptic events, which may contributeto serious neurological and medical sequelae or even death associatedwith SE.

Sudden Unexpected Death in Epilepsy, or “SUDEP,” another extremeepileptic event, is a phenomenon in which a patient with epilepsy diesunexpectedly and without an apparent, outstanding cause—that is, thedeath is unexplained since autopsy results are unrevealing. One of themain risk factors for SUDEP is the lack of seizure control with firstline drugs prescribed alone or in any safe combination and dosage.Whether or not the first in a chain of ultimately fatal events leadingto SUDEP is a seizure, the defining event is likely to be either cardiac(e.g., ventricular fibrillation or asystole) or respiratory (e.g.,apnea) or both. Currently, the monitoring, detection, prediction andprevention of SUDEP are inadequate and markedly limited in breadth anddepth of scope, as demonstrated by the fact that such deaths are, bydefinition, unexpected.

SE and/or CSE alter autonomic nervous system function, and SUDEP may becaused by autonomic dysfunction. Brain/neurological activity, such aselectrical activity, whether normal or abnormal, and autonomic functions(e.g., cardiovascular activity, respiration, etc.), referred to hereinas body signals, are functionally tightly coupled, and monitoring thesebody signals may provide valuable information. SE and CSE also increasethe risk of body injuries associated with seizures which may result fromthe impairment of the patient's consciousness or awareness. Injuriessuch as bone fractures and burns, for example, and adverse changes inbody functions during a seizure, may increase the risk of mortality tothe patient independent of the seizure itself. Such injuries may qualifya seizure as an extreme event regardless of its severity or closeness intime to a prior seizure (inter-seizure interval). On the other hand,certain seizure severity or inter-seizure interval values may suffice toclassify a seizure as an extreme event irrespective of the impact onbody functions/systems.

SUMMARY OF EMBODIMENTS

In one embodiment, the present disclosure relates to a method foridentifying changes in an epilepsy patient's disease state. The methodcomprises receiving at least one body data stream; determining at leastone of an autonomic index, a neurologic index, a metabolic index, anendocrine index, a tissue index, or a tissue stress index, a physicalfitness or body integrity index based upon the at least one body datastream; detecting a plurality of seizure events based upon the at leastone determined index; determining at least one seizure metric value foreach seizure event in the plurality of seizure events; performing afirst classification analysis of the plurality of seizure events basedon the at least one seizure metric value for each seizure event;detecting at least one additional seizure event based upon the at leastone determined index; determining at least one seizure metric value foreach of the at least one additional seizure events, performing a secondclassification analysis of the plurality of seizure events and the atleast one additional seizure event based upon the at least one seizuremetric value; comparing the results of the first classification analysisand the second classification analysis; and performing a further action.The further action may be selected from reporting a change from thefirst classification to the second classification; reporting the absenceof a change from the first classification to the second classification;displaying a result of at least one of the first classificationanalysis, the second classification analysis, and the comparing;identifying the emergence of a new class based on the comparing;identifying the disappearance of a prior class based on the comparing;identifying one or more outlier seizure events not part of any class;identifying an effect of a therapy; providing a therapy to the patientin response to the comparing; identifying a proposed change in therapy;identifying a proposed additional therapy; identifying an extremeseizure event; issuing a warning if a new seizure class appears or anextreme event occurs; and logging to memory the time, date and type ofchange in the patient's seizures.

In one embodiment, the present disclosure relates to a method comprisingidentifying at least three initial seizure events in a patient,classifying each initial seizure event into at least a first class,identifying at least one additional seizure event, re-classifying thefirst class based upon at least one of the initial seizure events andthe at least one additional seizure event; and performing a responsiveaction based upon the re-classifying.

In one embodiment, the present disclosure relates to a method comprisingdetecting a plurality of seizure events based upon body data of thepatient; determining, for each seizure event, at least one seizuremetric value characterizing the seizure event, where each of the atleast one seizure metric values comprises one of an autonomic index, aneurologic index, a metabolic index, an endocrine index, a tissue index,or a tissue stress index; performing a first classification analysis ofa first portion of the plurality of seizure events, the classificationanalysis comprising assigning each seizure event in the first portion toat least one seizure class based upon the proximity of the seizuremetric values to each other; performing a second classification analysisof a second portion of the plurality of seizure events, theclassification analysis comprising assigning each seizure event in thesecond portion to at least one seizure class based upon the proximity ofthe seizure metric values, wherein said second portion comprises atleast one seizure event not present in the first portion; comparing theresults of the first classification analysis and the secondclassification analysis; and performing a further action. The furtheraction may be an action selected from: reporting a change from the firstclassification to the second classification; reporting the absence of achange from the first classification to the second classification;displaying a result of at least one of the first classificationanalysis, the second classification analysis, and the comparing;identifying the emergence of a new class based on the comparing;identifying the disappearance of a prior class based on the comparing;identifying one or more outlier seizure events not part of any class;identifying an effect of a therapy; providing a therapy to the patientin response to the comparing; identifying a proposed change in therapy;identifying a proposed additional therapy; identifying an extremeseizure event; identifying a worsening trend in the patient's seizures;identifying an improvement trend in the patient's seizures; downgradingthe patient's condition in response to a worsening in the patient'sseizures; and upgrading the patient's condition in response to animprovement in the patient's seizures.

In one embodiment, the present disclosure relates to a method comprisingdetecting a plurality of seizure events in a first time period, whereineach of the seizure events is detected based upon body data of thepatient; determining at least one seizure metric value for each seizureevent of the plurality of seizure events; performing a firstclassification analysis of a first portion of the plurality of seizureevents, wherein the detection of each seizure in the first portionoccurred within a second time period within said first time period,wherein said first classification analysis comprises identifying atleast a first seizure class and a second seizure class based on the atleast one seizure metric value, wherein the second seizure classcomprises seizures that are more severe than seizures in the firstseizure class; performing a second classification analysis of a secondportion of the plurality of seizure events, wherein the detection ofeach seizure in the second portion occurred within a third time period,and wherein the third time period is a period within the first timeperiod, and at least a portion of the third time period is not withinthe second time period, wherein the second classification analysiscomprises determining, for each seizure event in the third time period,whether the seizure event is within the first seizure class and withinthe second seizure class, based on the at least one seizure metricvalue; identifying at least one of a change in the first seizure classand the second seizure class between the first classification analysisand the second classification analysis; and performing a responsiveaction based on the identifying.

In one embodiment, the present disclosure relates to a non-transitorycomputer readable program storage unit encoded with instructions that,when executed by a computer, perform a method as described above and/orherein.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, inwhich like reference numerals identify like elements, and in which:

FIG. 1 illustrates a medical device for detecting and classifyingseizure events related to epilepsy, and comparing classificationanalyses of seizure events, according to an illustrative embodiment ofthe present invention;

FIG. 2 illustrates a medical device system for detecting and classifyingseizure events related to epilepsy from sensed body data processed toextract features indicative of aspects of the patient's epilepsycondition;

FIG. 3 provides a stylized diagram of a medical device and differentdata acquisition units that may provide output(s) used by a body indexdetermination unit, in accordance with one illustrative embodiment ofthe present invention;

FIG. 4 provides a stylized diagram of a medical device and differentdata acquisition units that may provide output(s) used by a body indexdetermination unit, in accordance with one illustrative embodiment ofthe present invention;

FIG. 5A provides a stylized diagram of a seizure severity index unit fordetermining a seizure severity index using body data and/or seizuredata, in accordance with one illustrative embodiment of the presentinvention;

FIG. 5B provides a stylized diagram of a patient seizure impact unit fordetermining a patient impact using body data and/or seizure data, inaccordance with one illustrative embodiment of the present invention;

FIG. 5C provides a stylized diagram of an inter-seizure interval indexunit for determining a time elapsed between seizures, or inter-seizureinterval (ISI), using body data and/or seizure data, in accordance withone illustrative embodiment of the present invention;

FIG. 5D provides a stylized diagram of a Time of Occurrence Unit fordetermining a time of occurrence of a seizure, in accordance with oneillustrative embodiment of the present invention;

FIG. 6 provides a stylized diagram of an event/warning unit for warningof, and/or taking other action in response to a patient's seizure, inaccordance with one illustrative embodiment of the present invention;

FIG. 7 provides a flowchart depiction of a method for classifyingseizure events, in accordance with one illustrative embodiment of thepresent invention;

FIG. 8 provides a flowchart depiction of a method for implementingresponsive actions (warning, treatment, or data logging, among others)in response to determining that extreme events are probable, areoccurring, or have occurred, in accordance with one illustrativeembodiment of the present invention;

FIG. 9 provides a flowchart depiction of a method for warning and/orproviding a treatment to a patient likely to be in or recently have beenin an extreme epileptic event, in accordance with one illustrativeembodiment of the present invention;

FIG. 10 illustrates a stylized diagram of determining and executing atreatment plan by a healthcare provider, caregiver, and/or patientsubsequent to overriding automated treatment, in accordance with oneillustrative embodiment of the present invention;

FIG. 11 provides a flowchart depiction of a method for identifyingand/or managing a seizure event, in accordance with one illustrativeembodiment of the present invention;

FIG. 12 provides a stylized depiction of a graph relating to anexemplary seizure classification analysis, in accordance with oneillustrative embodiment of the present invention;

FIG. 13 provides a graphical exemplary representation of the probabilitydensity functions of seizure severity, in accordance with oneillustrative embodiment of the present invention;

FIG. 14 provides a graphical representation of exemplary probabilitydensity functions of inter-seizure intervals, in accordance with oneillustrative embodiment of the present invention.

The invention is susceptible to various modifications and alternativeforms. Specific embodiments have been shown by way of example in thedrawings and are herein described in detail, but are not intended tolimit the invention to the particular forms disclosed, but on thecontrary, the intention is to cover all modifications, equivalents, andalternatives falling within the spirit and scope of the invention asclaimed.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Illustrative embodiments of the invention are described herein. In theinterest of clarity, not all features of an actual implementation aredescribed in this specification. It will be appreciated that such adevelopment effort, while possibly complex and time-consuming, wouldnevertheless be a routine undertaking for persons of ordinary skill inthe art having the benefit of this disclosure.

This document does not intend to distinguish between components thatdiffer in name but not function. In the following discussion and in theclaims, the terms “including” and “includes” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to. “Couple” or “couples” are intended to mean either a director an indirect electrical connection. “Direct contact,” “directattachment,” or providing a “direct coupling” indicates that a surfaceof a first element contacts the surface of a second element with nosubstantial attenuating medium there between. The presence of smallquantities of substances, such as bodily fluids, that do notsubstantially attenuate electrical connections does not vitiate directcontact. “Or” is used in the inclusive sense (i.e., “and/or”) unless aspecific use to the contrary is explicitly stated. “Adapted to” and“capable of” as used herein may imply, among other things, that a devicehas a structure sufficient to perform some task or operation, and arenot used to state (implicitly or explicitly) mere intended uselimitations in the description and claims of the instant application.

The term “electrode” or “electrodes” described herein may refer to oneor more stimulation electrodes (i.e., electrodes for delivering atherapeutic signal to a tissue), sensing electrodes (i.e., electrodesfor sensing a physiological indication of a patient's body), and/orelectrodes that are capable of both delivering a therapeutic signal andsensing body signals.

“Specific care” in the context of epilepsy patients may be patient carethat is targeted at a seizure event itself, such as electricalstimulation, seizure drug treatments, and the like. “Supportive care”for epilepsy patients may involve care targeted to supporting ormaintaining vital function(s) within their normal ranges (e.g.,temperature, breathing, blood oxygenation, heart rate, blood pressure,acid-base balance, and electrolytes, among others, and minimizing therisk of tissue damage through, e.g., body and/or brain cooling, oradministration of medications with antioxidant properties and/or thelike).

The term “occurrence” used in reference to epileptic events may mean arisk of occurrence, an increased/increasing risk of occurrence, or anactual occurrence of such events. The terms “seizure event” and“epileptic event” may be used interchangeably.

The terms “microscopic,” “mesoscopic,” and “macroscopic” may refer totime periods for observation or analysis of seizure events and/orextreme seizure events, body changes such as heart wave and heart wavecomplex morphology, heart rate variability, and/or other body datadescribed herein. “Microscopic” may correspond to the scale ofobservation of at least part of a heart beat cycle, such as a P-wave, aQRT complex, a T-wave, a PQ interval, an ST segment, etc. Microscopicmay also correspond to a period of time that is less than a “mesoscopic”time period (e.g., less than 10 seconds). “Mesoscopic” may correspond toa scale of observation of several seconds to tens of seconds (e.g.,10-300 seconds), which may, for example, capture a change in a patient'sheart rate plot representative of a state change. “Macroscopic” maycorrespond to a scale of observation longer than 300 seconds that may beused to encompass more than the information contained in the“mesoscopic” scale or window as described above. In the context of thedescription provided herein, the term “window” may be used to refer toone or more of the “mesoscopic,” “microscopic,” and “macroscopic” timeperiods described above.

Seizure events may be detected based on effects of the seizure upon oneor more body systems of the patient. For example, seizures may beidentified from brain wave changes as measured by the patient's EEGsignal. In some patients, seizure events are accompanied by tachycardiabefore, at the same time as, or after the electrographic onset of theseizure. Automated systems have been proposed to detected seizureevents. As shown in FIG. 2, one such system may involve a medical devicesystem that senses body signals of the patient—such as brain or cardiacactivity shown in the figure—and analyzes those signals to identify oneor more aspects of the signal that may identify the occurrence of aseizure. The signal may be processed to extract (e.g., mathematically byan algorithm that computes certain values from the raw or partiallyprocessed signal) features that may be used to identify a seizure whencompared to the inter-ictal state. As shown in the right side of FIG. 2,the features may also be graphically displayed either in real time orsubsequent to the event to enable visual confirmation of the seizureevent and gain additional insight into the seizure (e.g., by identifyinga seizure metric associated with the seizure).

A seizure metric as used herein refers to a quantitative,semi-quantitative, or qualitative value that indicates some aspect of aseizure event. Seizure metrics may be endogenous (derived from bodysignals of the patient that are associated with a seizure event) orexogenous (independent of body signals of the patient). Exogenousmetrics may include, for example, the time of day at which a seizureevent is detected, or a noise level, lighting condition, or other aspectof the patient's environment at the time of the seizure.

In some embodiments, the seizure metric may indicate how severe aseizure event is, e.g., by one or more of the magnitude and duration ofthe seizure effects on the patient's heart rate, EEG, body movements,responsiveness, blood pressure, temperature, etc., or by whether theseizure was accompanied by a fall. Seizure metrics indicating theseverity of a seizure event are referred to herein as SSI metrics orindices, or more simply as SSI values or SSIs. In some embodiments, aseizure metric may indicate the time elapsed since a prior seizure eventoccurred, also known as an inter-seizure interval or ISI. In someembodiments, the seizure metric may indicate an impact that a seizurehas on the patient, also referred to herein as a seizure impact orpatient impact (PI). In some embodiments, a seizure metric may indicateother data associated with the seizure, such as whether the seizureevent occurred at a particular time in the patient's circadian rhythm(e.g., the time of day or night the seizure occurred, whether thepatient was awake or sleep, whether the patient was standing, sitting,or lying down, etc.).

A number of seizure metrics (e.g., one or more SSIs, ISIs, PIs, or otherdata such as time, posture, or patient environment data) may be used tocharacterize a particular seizure event. In some embodiments of theinvention, a plurality of seizure metrics may be used as a matrix tocharacterize a seizure event, and a plurality of seizure events may beclassified into one or more seizure classes. Seizure metrics may bestored in a memory of a medical device (MD) system, and in someembodiments may be transmitted to a user for analysis and/or display.

A patient may have certain kinds of seizures which may be classified as“extreme.” Extreme seizures may be identified based on certain seizuremetrics (e.g., SSIs, ISIs, PIs, time data, or other data). For example,a fall caused by a seizure may result in a skull fracture with brainlaceration and/or hemorrhage, or a fracture of some other bone,resulting in a high patient impact (PI) value. Even if other seizuremetrics (e.g., SSI or ISI values) might indicate a mild or non-extremeseizure, the patient impact (PI) may result in the seizure beingclassified as extreme in certain embodiments. Seizures may also causeother severe injuries such as burns or pulmonary edema. Similarly,extreme events may be characterized by low PI values but one or morehigh SSI or ISI values.

Seizure metrics in some embodiments refer to qualitative and/orquantitative data derived from body data recorded proximate (i.e.,shortly before, during, or shortly after) a seizure. In someembodiments, seizure metrics may be derived from cortical electricalsignals (e.g., total energy or maximum energy in the EEG signal fromseizure onset to seizure end). In other embodiments, seizure metrics maybe derived from non-cortical (e.g., non-EEG) autonomic signals, such ascardiac signals (e.g., increase or decrease in heart rate occurringduring a seizure above baseline heart rate prior to the seizure, theheart rate increase/decrease above the baseline rate multiplied by theduration of the heart rate remaining above a baseline heart rate, area“below the curve” of heart rate versus time for the duration of theseizure). In still other embodiments, seizure metrics may be derivedfrom accelerometer signals (e.g., acceleration and/or postural changeindicative of a fall, maximum acceleration during a seizure, or durationof acceleration above a threshold), from electromotor (EMG) signals(e.g., maximum magnitude of a muscular contraction above a thresholdvalue associated with normal physiological muscular contraction), ormany other body signals such as body temperature, skin resistivity, andthe like.

The body signals from which seizure metrics are determined may compriseone or more of autonomic (e.g., cortical electrical activity (EEG),heart beat, heart rate, heart rate variability, blood pressure,respiration, blood gases concentrations, or temperature), neurologic(e.g., kinetic signals such as accelerometer signals and/or inclinometersignals), responsiveness/awareness (e.g., complex reaction time signalsor test results), endocrine signals (e.g., hormone or neurotransmitterconcentrations), metabolic signals (e.g., lactic acid concentrations orCK (creatine kinase) concentrations), and/or tissue stress markersignals.

Seizure metrics may include seizure severity indices (SSIs),inter-seizure intervals (ISIs, defined as the time (in seconds orminutes) elapsed between the onset of consecutive seizures), PatientImpact (PI) values (such as a value indicative of a broken bone or otherinjury to the patient as a consequence of the seizure), or other dataassociated with particular seizures (e.g., the time of day at which theseizure occurs, the time elapsed between a therapy administered to thepatient and a seizure onset, whether a therapy was administered inresponse to the detection of the seizure). Information on determinationof SSI, ISI, PI, and other seizure metrics are provided in parent U.S.application Ser. Nos. 12/896,525 filed Oct. 1, 2010, 13/098,262 filedApr. 29, 2011, 13/040,996 filed Mar. 4, 2011, 13/091,033 filed Apr. 20,2011, and 13/333,235 filed Dec. 21, 2011, each hereby incorporated byreference herein in its entirety.

In some embodiments, values of seizure metrics may be used to classifyseizure events into one or more classes. In a particular embodiment, oneseizure class may include at least one class of extreme seizure events.In one embodiment, extreme seizures may be identified as those seizuresthat are more than two standard deviations above the mean for SSI or ISI(e.g., seizures having a maximum HR more than two standard deviationsgreater than the mean reference, or having an ISI from a prior seizureless than two standard deviations below the mean reference, of allseizures under consideration in a group of seizures). Additionally,seizure metrics may include indications of the impact of a seizure onone or more body systems (e.g., neurology, cardiovascular,musculoskeletal systems, etc.). The seizure metric may be determinedusing other neurologic, autonomic, tissue stress markers, endocrine,metabolic, or musculo-skeletal signals or status, among others.

In some embodiments, seizure metric values may be used to classifycertain seizures as non-extreme seizures, as tonic-clonic seizures, assimple partial seizures, as complex partial seizures, or many othertypes of seizures.

In some embodiments, patient seizure impact (PI) may be used to classifyseizures as extreme. Seizures may be “extreme” regardless of SSI and/orISI values, if they result in system dysfunction of a type, magnitude,duration and/or frequency exceeding the ictal or post-ictal baselinedysfunction for that subject, or if the seizure causes the subject tosustain injuries. In one embodiment, PI may be a scalar-valued functionof one or more body data variables that simplifies a possibly complexset of body information down to a single number.

PI refers to effects of a seizure closely correlated in time with itsoccurrence. Measures of patient seizure impact (PI) collected over timemay be used to determine patient seizure burden (PB), which is thecumulative effects of temporally close as well as remote seizures. PIand/or PB may provide information with regard to the effect of one ormore seizures upon one or more parts of the body.

The PI may be a function of the health of a particular patient. In thismanner, in one embodiment, the PI may reflect a more severe effectexperienced by a first patient suffering a seizure of a first SSI, andreflect a less severe effect in a second patient who experienced aseizure of a second SSI, wherein the second SSI is substantially similarto the first SSI. The terms “patient impact”, “seizure impact”, and“patient seizure impact” may be used interchangeably herein and referredto as “PI”. In one embodiment, the PI may be any statistic (orscalar-valued function) associated with a seizure that reflect someaspect of the seizure's impact. PI values and may be ordered/sorted orranked so that the differences between different seizures can bemeasured, compared, and/or interpreted to provide meaningfulinformation.

Classifying a seizure event as “extreme” may be based upon a deleteriousimpact upon (or seriousness in relation to) the patient's health (e.g.,falls, bone fractures, cardiac and/or respiratory dysfunction, memoryloss, etc.) and wellbeing (e.g., depression), or the condition of thepatient's disease state (e.g., worsening of epilepsy). In differentcases, extreme seizure events may be classified according to otherstandards as well, and need not necessarily be specifically limited tothose described herein. Similarly, extreme seizure events may be acombination of the above described classifications. An extreme seizureevent (e.g., status epilepticus, or a seizure causing a fall and headtrauma) may result in coma, cardio-respiratory failure, metabolicacidosis, liver and/or renal failure, bed sores, bone fractures, tissuehypoxia, and/or brain damage.

In one embodiment, the approach of treating certain seizures as extremeevents lends itself to a statistical or probabilistic approach for theprevention of status epilepticus through their anticipation or earlydetection.

More generally, seizures may be classified into a wide variety ofclasses including, by way of non-limiting examples: extreme &non-extreme seizures; generalized and partial seizures; tonic,tonic-clonic, absence, and other diagnostic classes; seizures caused byparticular events (e.g., flashing lights as recorded by photometer, loudnoises as detected by a microphone); seizures resulting in cardiacdysfunction (e.g., arrhythmias, EKG morphology changes, heart ratevariability (HRV) changes, etc.); seizures having an abnormally longperiod of ictal impairment as measured by, e.g., one or moreresponsiveness/awareness tests; etc.

The following exemplary “seizure metrics” alone or in any combinationmay be used to classify a seizure or seizures in one or more seizureclasses by quantifying one or more of the following:

1. Magnitude and/or rate of increase in seizure energy or intensity(EEG); seizure duration (from time of detection to seizure end time);extent of seizure spread (note that one type of seizure severity indexmay be derived from the values of at least two of these three metrics),seizure magnitude, rate of change (e.g., drop in energy from seizure tothe post-ictal state), duration in post-ictal state energy compared tointer-ictal or ictal energy, and/or magnitude and rate of energyrecovery from the post-ictal to the inter-ictal state; 2. Inter-seizureinterval (time elapsed since the last seizure) duration; predicted ISIto the next seizure, including the conditional probability of time tothe next seizure given the time elapsed since the last seizure; 3.Seizure frequency per unit time, cumulative intensity, duration, extent,spread, and/or seizure severity index (SSI) per unit time; 4. Cumulativemagnitude, duration, and rate of the change in post-ictal energy perunit time compared to inter-ictal or ictal energy for the patient,and/or magnitude, rate, and extent of spread of changes in post-ictalenergy compared to the inter-ictal or ictal states; 5. Magnitude,duration, and/or rate of change in level of consciousness (as measured,for example, using available coma scales such the Glasgow scale orqualitative classification (e.g., deep coma, superficial coma, stupor,lethargy, awake but confused)) as also used in clinical practice,compared to a baseline consciousness level; 6. Magnitude, duration (whenapplicable, e.g., when the patient is awake), and/or rate of change inone or more cognitive functions as measured, for example, using areaction time (complex or simple) or any other validatedneuropsychologic test; 7. Magnitude, duration, and/or rate of change inautonomic indices such as heart rate, heart rate variability, heartrhythm, EKG, EKG morphology (e.g., changes in one or more cardiacparameters such as the QRS complex, P waves, T waves, QT segment length,PQ segment length), blood pressure, respiration, catecholamines,temperature and/or galvanic skin resistance, among others; 6. Magnitude,duration, and/or rate of change in metabolic indices such as arterialpH, SaO2, CO2, glucose and/or electrolytes, among others; 7. Magnitude,duration, and/or rate of change in endocrine indices such prolactin,cortisol, and/or growth hormone, among others; and 8. Magnitude,duration, and/or rate of change in tissue stress markers, such asReactive oxygen and nitrogen species, including but not limited to iso-and neuro-prostanes and nitrite/nitrate ratio, glutathione, glutathionedisulfide, and glutathione peroxidase activity, citrulline, proteincarbonyls, thiobarbituric acid, the heat shock protein family,catecholamines, lactic acid, N-acetylaspartate, free radicals, CK,Aldolase, troponin, and/or the like, or of their metabolites, whenapplicable.

Additional seizure metrics may also be used in certain embodiments ofthe present invention to classify seizure events, including time of dayat seizure onset, seizure end, and/or inter-ictal state end, state ofthe patient (awake vs. asleep) at seizure onset, level of physicalactivity at the time the measurements were made, or patient age, gender,and/or health status.

In one or more embodiments, signals or index values indicative of one ormore of autonomic, neurologic, endocrine, metabolic, andgastro-intestinal system function, or of tissue/organ stress, such asthose listed below, along with processes and tools for measuring and/orderiving these signals and markers, may be used to derive one or moreseizure metrics, which may in turn be used to classify seizures into oneor more seizure classes:

I. Autonomic

-   -   a) Cardiac: phonocardiogram (PKG) values, Echocardiography        values, Apexcardiography (ApKG) values, intra-cardiac pressure,        cardiac volume, the ratio of intra-cardiac pressure to cardiac        volume, ejection fraction, blood flow, cardiac thermography,        heart rate (HR), heart rate variability (HRV), rate of change of        heart rate or HRV, heart sounds, heart rhythm, heartbeat wave        morphology, thoracic wall deflection;    -   b) Vascular: arterial pressure, arterial and/or venous blood        wave pressure morphology, arterial and/or venous blood flow        velocity, arterial and/or venous blood flow sounds, arterial        and/or venous thermography;    -   c) Respiratory: tidal volume, minute volume, respiratory wave        morphology values, respiratory sounds,        intercostalelectromyography (EMG), diaphragmatic EMG, at least        one chest wall and/or abdominal wall motion, change of RR, rate        of change of RR, arterial gas concentrations, oxygen saturation,        end-tidal CO₂, blood pH;    -   d) Dermal: skin resistance, skin temperature, skin blood flow,        sweat gland activity;    -   e) Neurotransmitters: concentrations of catecholamines and/or        catecholamine metabolites, acetylcholine and/or        acetylcholinesterase activity in blood and/or saliva, rate of        change cathecholamines, acetylcholine and/or        acetylcholinesterase activity;

II. Neurologic

-   -   a) Cognitive/Behavioral: level of consciousness, level of        attention, reaction time, memory, visuo-spatial, language,        reasoning, judgment, calculations, auditory and/or visual        discrimination;    -   b) Kinetic: force of contraction, body movement direction,        speed, acceleration, trajectory in one, two and/or three        dimensions, pattern and/or quality, posture, orientation,        position, body part orientation and/or position in reference to        each other, body part orientation and/or position in reference        to one or more predetermined axes or fiducials, muscle tone,        agonist-to-antagonist muscle tone relation, gait, accessory        movements, falls;    -   c) Vocalizations: formed and/or unformed vocalizations;    -   d) Electroencephalography (EEG)/Electrocorticography (ECoG),        evoked potentials, field potentials, single unit activity;

III. Endocrine

-   -   a) prolactin, luteinizing hormone, follicle stimulation hormone,        growth hormone, ACTH, cortisol, vasopressin, beta-endorphin,        beta, lipotropin, corticotropin-releasing factor (CRF);

IV. Tissue Stress Markers

-   -   a) reactive oxygen and/or nitrogen species from the list        comprising iso- and neuro-prostanes and nitrite-nitrate ratio,        glutathione, glutathione disulfide and glutathione peroxidase        activity, citrulline, protein carbonyls, thiobarbituric acid, a        heat shock protein family, catecholamines, lactic acid,        N-acetylaspartate, metabolites of citrulline, protein carbonyls,        thiobarbituric acid, a heat shock protein family,        catecholamines, lactic acid, N-acetylaspartate;

V. Metabolic:

-   -   a. arterial pH, arterial gases, lactate-pyruvate ratio,        electrolytes and glucose; and/or

VI. Musculo-Skeletal:

-   -   a. muscle mass, bone mass, bone density, bone fractures.

In human patients with pharmaco-resistant seizures, the probabilitydensity functions of energy and inter-seizure intervals of seizuresoriginating from discrete brain regions may be partly described by powerlaws. The probability density function of seizure energy, a power law,differs from a Gaussian or normal probability density function in itsskewness (to the right with respect to the mean), reflecting thepresence of events with very large (“extreme”) energy. For example, ifseizure energy or severity is above two standard deviations from themean (calculated from a normalized distribution), the seizure isconsidered as an extreme event, including but not limited to, statusepilepticus, a risk of status epilepticus, an increased risk of statusepilepticus, a risk of SUDEP, or an increased risk of SUDEP, and/or thelike. Inter-seizure intervals with duration below two standarddeviations to the left of the mean calculated from a normalizeddistribution may be indicative of an extreme epileptic event/stateincluding but not limited to, status epilepticus, a risk of statusepilepticus, an increased risk of status epilepticus, a risk of SUDEP,or an increased risk of SUDEP, and/or the like. Alternatively, anextreme event may correspond to that with severity as measured by anybody signal, at or below the 10th percentile or at or above the 90thpercentile of values, for the time of day (to account for circadianvariability), or state (e.g., wakefulness versus sleep) or level ofphysical activity (moving about versus resting) and patient areclassified as extreme. Other values for classification of events may bechosen as needed to improve performance. It should be noted, however,that in one or more embodiments no formal statistical analysis needs tobe made to have extreme event(s); statistical significance is notnecessarily needed to reach a conclusion that an extreme event hasoccurred.

With respect to extreme seizure events and/or patient mortality, statusepilepticus (SE) may be an independent predictor of death. As comparedwith a first brief epileptic seizure, a first SE episode seems toincrease the risk of developing epilepsy. Where prior studiesincorrectly focused exclusively on seizure duration as an indicator ofpatient risk, a more useful and correct approach would also include aseizure severity index (SSI), calculated based on at least one of theautonomic, neurologic, endocrine, or metabolic indices, or tissue stressmarkers.

For example, heart rate, an autonomic index, may be used to compute aseizure severity index. In one example, a patient's mean interictal(in-between seizures) heart rate prior to a seizure is 80. Peak ictalheart rate for the seizure is 150 bpm, and the ictal increase in heartrate lasts for 40 sec. SSI may be calculated as either 6000 if the peakheart rate and seconds are used (150 bpm×40 sec), 100 if peak HR andminutes are used (150 bpm×⅔ min), 2800 if the net HR increase andseconds are employed ([150 bpm−80 bpm]×40 sec), or 46.67 if net HRincrease and minutes are employed ([150 bpm×80 bpm]×0.67 min). Differentbut equivalent measures may be used by employing different units of time(e.g., seconds, minutes or hours).

In another embodiment, the “area under the curve” (rather than peak HRor net increase in HR over the duration of the seizure) may be alsoutilized to compute a particular SSI. Such an SSI indication may be usedin some embodiments to take into account differences in a) the rates ofincrease in HR during seizures and non-seizure (e.g., exertional)tachycardia, and b) differences in the rate of HR return to theinterictal baseline following a seizure in contrast to HR decreasefollowing exertion (e.g., exercise) or postural changes (e.g., standingfrom a lying or sitting position). In other embodiments, measures suchas the time from the detection of seizure onset to the peak HR duringthe seizure, and the time from peak HR during the seizure to the returnto interictal baseline (or to within a desired percentage, e.g., 110% ofthe interictal baseline HR) may also be used as SSI indices.

As another example, blood oxygen saturation (SaO2) may be used tocompute a seizure severity index. If a patient's mean interictal oxygensaturation during wakefulness is 93%, and during a convulsion it dropsto a minimum of 60%, remaining below the interictal baseline for 60sec., SSI values based on this index, may be 36 (0.60 minimum SaO2during the seizure×60 seconds seizure duration), or 19.8 ([93% baselineSaO2−60% minimum ictal SaO2 during seizure]×60 seconds seizure duration)if the net decrease in ictal SaO2 from baseline SaO2 is used. Equivalentmeasures may of course be obtained by using different units of time(e.g., minutes instead of seconds for seizure duration) or SaO2. Similarto the “area under the curve” described above for HR, the “area underthe curve” for SaO2 during a seizure may also be used to obtain SSImeasures that take into account how fast SaO2 falls below, and returnsto, the interictal baseline during and after the seizure. Additionally,the elapsed time for SaO2 to fall from the interictal baseline to theminimum SaO2 during the seizure, as well as the time required for SaO2to return to interictal baseline from the minimum ictal SaO2 value, mayalso be used as SSI indices.

In one embodiment, seizure severity index (SSI) values indicative of theseverity of a seizure may be determined based upon body data asdescribed above. In one embodiment, the determined SSI value(s) may becompared to reference/extreme reference values that may or may notinclude a status epilepticus value. The status epilepticus value(s) maybe based upon at least one of a past SSI value, a mean SSI value, amedian SSI value, a mode SSI value, a percentile SSI value, a normalizedSSI value, a distribution of SSI values, or to any other statisticaltransformation of an SE index or observable SE index change.

The increased probability of subclinical and clinical pharmacoresistantseizures to occur closely spaced in time (i.e., in temporal clusters),an observation previously made for clinical seizures only, as well asthe decreasing probability of seizure occurrence as the time since thelast seizure increases, may be interpreted as: (i) reflecting theinherent capacity of seizures to trigger other seizures; (ii) indicatingsome form of seizure interdependency or plasticity (“memory”) in thesystem; and/or (iii) a clinically useful observation that in theembodiments disclosed herein may be exploited to anticipate and preventextreme epileptic events, including but not limited to statusepilepticus.

In some embodiments, the present invention determines one or more datapoints associated with an event detected from body data of an epilepsypatient. In some cases the data points may be seizure metric data valuesassociated with a seizure, and the seizure may be classified into one ormore seizure classes based upon the seizure metrics. Seizure metric datamay be used to classify the seizure based upon 1) data associated withthe seizure itself, or 2) based upon data not directly related to theseizure, but associated with the seizure event (e.g., data from thepatient's environment such as sound, noise, temperature, or humidity, orpatient-specific data such as level of fitness, fatigue, etc.). In somecases, the data associated with the event may indicate that the patienthas had a seizure that has resulted in unforeseen and/or atypical orundesirable (negative) consequences for the patient. Such events mayinclude, by way of non-limiting examples, a fall resulting in a brokenbone or metabolic derangement (transient severe metabolic acidosis, or aworsening of autonomic function (e.g., decrease in heart ratevariability)) over time such that the patient's risk profile for one ormore adverse events has increased, given the patient's seizure type.

While status epilepticus is one example of an extreme event, many otherevents that an epileptic patient may have may also present an elevatedrisk to health or safety, and may also be classified as extreme events.Accordingly, it will be appreciated that the terms “status epilepticus,”“extreme event,” and “extreme seizure” are not synonymous, although theymay in some instances be used herein in reference to the same event.Some embodiments of the present invention use neurologic and other typesof body signals (e.g., autonomic, metabolic) in a multi-variant,adaptive manner to optimize sensitivity and specificity of detection ofSE and CSE, and more importantly to anticipate SE and CSE and alsoSUDEP.

The invention also broadens diagnostic and therapeutic horizons byextending the concepts of extreme seizure and epileptic events tophenomena other than generalized or partial status epilepticus andsudden death. In some embodiments, the present invention involvesquantifying the impact of status epilepticus on one or more body systemsto either 1) prevent bodily functions from entering the extreme state(thus becoming extreme epileptic events) or 2) provide early treatmentto minimize the risk of mortality.

In some embodiments, the present invention involves detecting aplurality of seizure events based upon one or more body systems affectedby a seizure, and performing a classification analysis by classifyingthe seizure events into one or more classes. In particular embodiments,the classification analysis may be repeated as additional seizures aredetected, and changes in the seizure classes over time may beidentified. Changes in seizure classes may, in turn, be used to performa number of tasks, such as identifying extreme (and by extension,non-extreme) epileptic events, and identifying changes in the patient'sdisease state (e.g., whether the patient's epilepsy is improving,worsening, remaining the same, improving in some aspects but worseningin others, etc.).

In one more embodiments, the invention comprises a method identifying aplurality of seizure events, determining one or more seizure metricvalues for each of the seizures, performing a classification analysis ofeach of the plurality of seizure events, identifying one or moreadditional seizure events, determining one or more seizure metric valuesfor each of the additional seizure events, performing a secondclassification analysis, comparing the results of the first and secondclassification analyses, and taking one or more actions in response tothe comparison of the first and second classification analyses. Theresponsive action(s) may comprise one or more of reporting a change inone or more classes from the first classification analysis to the secondclassification analysis, reporting the absence of such a change,displaying a resulting of the first or second classification analyses orthe comparing, identifying a new class based on the comparing,identifying the disappearance of a class, identifying one or moreoutlier seizure events, identifying an effect of a therapy, providing atherapy in response to the comparing, identifying a proposed change intherapy, identifying a proposed additional therapy, or identifying anextreme seizure event.

Electrocardiography (ECG) indicators of pathologic cardiacrepolarization, such as prolongation or shortening of QT intervals aswell as increased QT dispersion, are established risk factors forlife-threatening tachyarrhythmia and sudden death. Abnormalities incardiac repolarization have recently been described in people withepilepsy. Importantly, periictal ventricular tachycardia andfibrillation have also been reported in the absence of any underlyingcardiac disease. Based on these abnormalities in cardiac repolarization,measures to reduce the risk of, or prevent, sudden death may includeanti-arrhythmic medication and implantation of cardiac combinedpacemaker-defibrillator devices.

Seizures are powerful biological stressors and inductors of stressmarker indices and deplete the body of certain anti-oxidants, such asglutathione peroxidase. Exemplary stress marker indices comprise changes(direction, rate, or magnitude) in glucose, prolactin, cortisol,catecholamines, chromogranin A, free radicals or reactive oxygenspecies, lactic acid, blood gases, N-acetylaspartate, in the expressionof heat shock proteins, or in metabolites of any or all thereof. Forexample, a “cortisol parameter” refers to a stress marker index relatingto cortisol or a metabolite thereof, and a “catecholamine parameter”refers to a stress marker index relating to a catecholamine or ametabolite thereof. The concentration of certain compounds that protectfrom biological stress (e.g., dehydroepiandrosterone or its sulfateconjugate, glutathione peroxidase) or the body's total antioxidantcapacity may be also measured to determine if it is adequate and if notto increase it using commercially or naturally available antioxidants tostall disease progression. Stress marker index indices and antioxidantsmay be measured in brain (invasively and/or non-invasively), CSF,plasma, serum, erythrocytes, urine, and saliva (e.g. alpha amylase).

Although not so limited, methods and apparatus capable of implementingembodiments of the present invention are described below. In the contextof this description, a medical device or medical system may be referredto as an implantable medical device and/or an implantable medicaldevice/system (IMD). It is contemplated that such a device and/or systemmay be implantable or non-implantable/non-implanted in variousembodiments without departing from the spirit and scope of theinvention. That is, when an implantable medical device/system (IMD) isdescribed in one or more embodiments, it is also contemplated that acorresponding non-implanted or non-implantable may be used in one ormore alternate embodiments.

FIG. 1 is a block diagram depiction of a medical device (MD) 100, inaccordance with an illustrative embodiment of the present invention. TheMD 100 may be fully implantable (such as an implantable vagus nervestimulation system, or deep brain stimulation system) in someembodiments, and fully or partially external to the patient's body inother embodiments.

The MD 100 may comprise a controller 110 capable of controlling variousaspects of the operation of the MD 100. The controller 110 may include aprocessor 115, a memory 117, as well as other common circuits associatedwith processors and integrated circuits (e.g., A/D converters, digitalsignal processors, etc.) for processing and storing data. Processor 115may comprise one or more microcontrollers, microprocessors, orequivalent circuitry capable of performing various executions ofsoftware and/or firmware. Memory 117 may store various types of data(e.g., internal data, external data instructions, software codes, statusdata, diagnostic data, etc.), and may include one or more of randomaccess memory (RAM), dynamic random access memory (DRAM), electricallyerasable programmable read-only memory (EEPROM), flash memory, etc.Memory 117 may be separate from, but communicatively coupled to thecontroller 110 in some embodiments (as shown in dotted line form in FIG.1), or may be integrated into controller 110 and/or processor 115.

The MD 100 may also comprise a power supply 130, which may comprise abattery, voltage regulators, capacitors, etc., to provide power for theoperation of the MD 100, including delivering the therapeutic electricalsignal. The power supply 130 in some embodiments may be rechargeable,while in other embodiments it may be non-rechargeable. The power supply130 provides power for the operation of the MD 100, including electronicoperations and the electrical signal generation and delivery functions.The power supply 130 may comprise a lithium/thionyl chloride cell or alithium/carbon monofluoride (LiCFx) cell if the MD 100 is implantable,or may comprise conventional watch or 9V batteries for external (i.e.,non-implantable) embodiments. Other battery types known in the art ofmedical devices may also be used.

The MD 100 may include one or more sensors 112 for sensing one or morebody data streams in some embodiments. The sensor(s) 112 are capable ofreceiving signals related to a body parameter, such as the patient'sheart beat, and delivering the signals to the MD 100. In one embodiment,the sensor(s) 112 may be implanted, such as electrode(s) 106, 108. Inother embodiments, the sensor(s) 112 are external structures that may beplaced on the patient's skin, such as over the patient's heart orelsewhere on the patient's torso. In some embodiments, lead 111 may beomitted and the MD 100 may communicate wirelessly with sensor 112.Various types of body data, such as cardiac data, respiration data, bodymotion or movement data, etc., may be provided by sensors 112.Controller 110 may be capable of receiving and/or processing body datareceived from sensors 112. Processor 115 may receive body data from oneor more modules or units within MD 100, may process the received data,and may provide data to one or more modules or units within MD 100. Insome embodiments, processor 115 may be integrated with one or more otherunits or modules of MD 100, described hereinafter.

In one embodiment, body data may be provided (e.g., from processor 115,memory 117, and/or sensors 112) to a body index determination unit 121,which may determine one or more body indices from the provided bodydata. In one embodiment, body index determination unit 121 may determineone or more body indices that may be indicative of a seizure event. Forexample, body index determination unit 121 may calculate or moreautonomic, neurologic, metabolic, tissue, or tissue stress indices thatmay be compared to corresponding thresholds to determine the onset of aseizure. Additional details regarding seizure detection using one ormore body indices are provided in the parent applications to the presentapplication, as referred to earlier. In one embodiment, a seizuredetermination unit 123 may determine whether or not a seizure event hasoccurred using one or more body indices determined by the body indexdetermination unit 121.

Embodiments of the present invention also involve characterizing seizureevents by determining one or more seizure metrics, and classifying aplurality of seizure events into classes based upon the seizure metrics.Referring again to FIG. 1, when a seizure event has been identified,body data may be provided (e.g., from processor 115, memory 117, and/orsensors 112) to a seizure metric determination unit (SMDU) 125 todetermine one or more seizure metric values associated with the seizureevent. In one embodiment, processor 115 may also provide additional data(e.g., time data, sound or environmental data) to SMDU 125, which maydetermine seizure metric values that are derived in whole or in partfrom non-body data. SMDU 125 may store the seizure metric data in memory117.

A variety of seizure metrics may be determined by MD 100 and used tocharacterize seizure events. SMDU 125 may comprise one or more sub-unitsto determine different kinds of seizure metrics. In one embodiment, SMDU125 includes a seizure severity index unit (SSIU) 126.SSIU 126 mayidentify one or more SSI values for a plurality of seizure events, andmay store those values in memory 117 for reporting to a user (e.g.,through a monitoring unit 170). SMDU 125 may also comprise anInter-Seizure Interval Unit (ISIU) 127, which may determine one or moretime periods between the detected seizure and a prior seizure event.Details on determining SSI and ISI values are provided in parent U.S.application Ser. Nos. 13/040,996 and 13/333,235.SMDU 125 may furtherinclude a patient seizure impact (PIU) 128 to identify seizure metricsindicative of a patient impact index. In some embodiments, SMDU 125 mayalso include a time of occurrence unit (TOU) 129 to identify a time ofoccurrence of the seizure. In one embodiment, TOU 129 may be part ofISIU 127. Additional sub-units of SDMU may also be provided to determineadditional seizure metric values, which may include, for example,sensors or transmitters/receivers to obtain data relating to thepatient's environment, location, quality of life (QOL) measures, etc.

In one aspect, the SMDU may comprise a SSIU 126 which may be used todetermine, from body data, one or more indices that describe orcharacterize a severity measure of a seizure event. SSIs may becalculated in some embodiments using one or more of a seizure intensitymeasure, a seizure duration measure, and an extent of seizure spreadmeasure based on one or more of autonomic, endocrine, metabolic, tissuestress marker signals. For example, SSI may be the product of a seizureintensity measure and a seizure spread measure, or the sum of a seizureintensity, seizure duration, and seizure spread measure from one or moreof the signals previously mentioned. An SSI may be a scalar-valuedfunction of one or more body data variables that simplifies a possiblycomplex set of body information down to a single number. An SSI may beany statistic (or scalar-valued function) associated with a seizure withthe property values that reflect some aspect of the severity of thecorresponding seizures and may be ordered/sorted so that the distancebetween the SSI values for different seizures can be measured, comparedand/or interpreted to provide meaningful information.

In one embodiment, the SSI may be a quantity whose variation over aperiod of time measures the change in some body data or body phenomenon.The SSI may also be a statistic associated with the seizure that enablescomparison between different seizures. The values for different seizuresmay be ordered/sorted or ranked and the distance (in a Euclidian ornon-Euclidian sense) between them may be measured/compared/interpretedto provide meaningful information. If the SSI values describe theseverity of the seizure not in absolute terms, but in a manner relativeto other seizures for that patient (or relative to other patients), theSSI may be referred to as a “Relative SSI.” In some embodiments, whenmore than one SSI is used at the same time, the plurality of SSIs may becombined into a single SSI by weighted averaging, and/or the like.

In one embodiment, SMDU 125 may include an inter-seizure interval unit(ISIU) 127. The ISIU 127 may determine an index based upon inter-seizureintervals between one or more seizures experienced by the patient. Theinter-seizure interval index may, in some embodiments, be representativeof the current inter-seizure interval relative to a past seizure event.This may comprise the immediately preceding seizure or the nearestseizure within the same class as the present seizure. ISIU 127 maydetermine an ISI value in real-time or off-line after the seizure eventhas occurred. ISIU 127 may determine ISI values based upon body datainformation, external indications (e.g., the patient's environment orsurroundings), a patient's past seizure data, a normalized seizure datadistribution, expected seizure data, and/or other data that would becomeapparent to one of skill in the art having the benefit of thisdisclosure.

In some embodiments, SDMU 125 may comprise a patient seizure impact unit(PIU) 128, which determines an impact of the seizure on a patient. PIU128 may determine a number of measures indicative of the impact of aparticular seizure on one or more systems of the patient's body. Thismay include cognitive test results, severity measures (e.g., maximumacceleration) associated with falls, indications of effects on cardiacfunction such as tachycardia, bradycardia, or asystole, effects onbreathing such as apnea periods. Trauma may also be determined by, forexample, a detected hard fall as measured by an accelerometer followedby one or more of an extended period in which the patient remains prone,a period of asystole, or a period of apnea. PIU 128 may also determineother indications of trauma such as burns, broken bones, etc.

Time of Occurrence Unit (TOU) 129 may comprise software, hardware,firmware, etc., that determines a time at which seizure determinationunit 123 identified the occurrence of a seizure. TOU 129 may provide oneor more of a time of day (e.g., 2:22:14 PM) of the seizure, a month,date and year the seizure occurred, as well as patient-specific measuressuch as how long the patient had been awake, upright, sleeping,reclining, etc. at the time of the seizure. Data from TOU 129 may beused, for example, to identify seizure triggers, periods of increased ordecreased risk of seizures, or to identify other temporal variablesaffecting the patient's disease state.

MD 100 may further comprise a seizure classification analysis (SCA) unit140 which may classify a plurality of seizures based upon the proximityof the seizures to one another in a seizure metric phase space. Suchclassifications based on the similarity of seizure in terms of one ormore seizure metrics (e.g., SSI, ISI, PI, time of occurrence, etc.) maybe an alternative to classifications based on pre-existing criteriaindependent of proximity in the seizure metric phase space. In oneembodiment, SCA unit 140 may create a multidimensional matrix for eachseizure from seizure metric values associated with that seizure, and theclassification analysis may comprise an n-dimensional analysis using nseizure metrics associated with the plurality of seizures. The analysismay comprise identifying one or more groups of seizures based on theirproximity to each other in the n-dimensional seizure phase space.Different analyses may be performed by selecting different seizuremetrics for each of the dimensions in the n-dimensional space. In oneembodiment the multidimensional matrix comprises an m-dimensionalmatrix, where m and n may be different and analyses may be performed byselecting n dimensions, corresponding to n seizure metric data points,within the m-dimensional matrix space maintained for the seizures.

In one embodiment, the SCA unit 140 may classify a plurality of seizureevents into one or more classes based on the seizure metric datadetermined by SMDU 125. In one embodiment, the one or more classes intowhich each of the plurality of seizure events may be classified may beclinical seizure classes, e.g., simple or complex partial seizures,primarily or secondarily generalized seizures, tonic seizures,tonic-clonic seizures, etc. In one embodiment, the one or more classesmay correspond to patient risk levels, e.g., mild seizure events orextreme seizure events. In still other embodiments, the one or moreclasses may correspond to particular seizure metrics, or combinations ofseizure metrics, such as seizures accompanied by tachycardia, breathingchanges, temperature changes, or seizures occurring in a particular timeof day (e.g., late morning, early afternoon, during sleep, etc.).

In some embodiments, SCA unit 140 may perform a seizure classificationanalysis in real time as seizure events are detected. In otherembodiments, the classification analysis may be performed off-line onstored seizure metric data. In one embodiment, SCA unit 140 may performa first seizure classification on a plurality of seizure events at afirst point in time. As additional seizures are detected, additionalseizure metric data may be determined by SMD unit 125 for the additionalseizures. SCA unit 140 may subsequently perform a second seizureclassification analysis at a second point in time after the firstseizure classification analysis. The second seizure classificationanalysis may be performed using seizure metric data on the seizuresanalyzed in the first analysis as well as on one or more additionalseizures occurring after the first seizure analysis.

In still further embodiments, SCA unit 140 may perform a first seizureclassification analysis on a plurality of seizures for a first definedtime period, e.g., for all seizures occurring in a particular week,month, quarter, year, etc. SCA unit 140 may further perform a secondseizure classification analysis on a second plurality of seizures for asecond time period. The second time period may include a time periodoccurring entirely after the first defined time period. In anotherembodiment, the second time period may include all or part of the firsttime period as well as an additional time period after the first timeperiod.

In some embodiments, SCA unit 140 may be programmed to perform a seizureclassification analysis at pre-specified intervals. SCA unit 140 mayalternatively perform a seizure classification analysis in response torequest for such an analysis by a user, or by the occurrence of aparticular event, e.g., by a seizure metric determination indicatingthat the patient has experienced a severe seizure.

MD 100 may further comprise a classification analysis comparator (CAC)142.CAC 142 may compare a first classification analysis to a secondclassification analysis and identify changes, differences, trends, orother measures of contrast between the first and second analyses. Thedifferences may in one embodiment include a change in a seizure classfrom the first analysis to the second analysis. Changes may include,e.g., that the class identified in the first analysis has grown, shrunk,increased or decreased in density, elongated, shifted in centroid, orhas changed in other aspects that may be mathematically and/orgraphically identified. In one embodiment, the first and second timeperiods may be non-overlapping time periods, and the CAC 142 mayidentify whether a seizure class in the first time period is present inthe second time period. The CAC 142 may further indicate change or trendin the patient's disease state from the first to the second time period.

In another embodiment, CAC 142 may, as a result of the comparison of thefirst classification analysis and the second classification analysis,determine that there are no significant differences between the firstand second analyses. In a still further embodiment, CAC 142 may comparea first and a second classification analysis and identify the emergenceof a new class in the second analysis that was not present in the firstanalysis, or the disappearance in the second analysis of a classidentified in the first analysis. CAC 142 may also identify one or moreoutlier seizure events that are not part of any class, and may identifyextreme seizure events, including but not limited to sub-classes ofextreme events. In another embodiment, the CAC 142 may identify aneffect of a therapy, or may identify additional therapies that may beproposed. Output values of CAC 142 may be stored in MD 100 in memory 117or other storage areas.

MD 100 may further comprise a communication unit 160 that may facilitatecommunications between MD 100 and various other devices. In particular,communication unit 160 is capable of transmitting and receiving signalsto and from a monitoring unit (MU) 170, such as a handheld computer, PDAor tablet that can communicate with the MD 100 wirelessly or by cable.Communication unit 160 may include hardware, software, firmware, or anycombination.

MU 170 may receive, process, display, and/or respond to data from SCAunit 140 and/or CAC 142. In one embodiment, MU 170 may display one ormore seizure metrics determined by SCA unit 140. In another embodiment,MU 170 may graphically present on a display 175 seizure metric data forat least a portion of the seizures in the first analysis and the secondanalysis to depict for a user a change in a seizure class over time.

In one embodiment, monitoring unit 170 may further comprise atlogging/reporting module 165. The logging/reporting module 165 may beadapted to log and/or store data related to the patient, the patient'sphysical condition, the patient's disease and disease state and/or anyother body data. The logging/reporting module 165 may store informationrelating to the patient's disease (e.g., seizure events, data related totime of recovery after seizure events and/or patient sleep cycles). Thelogging/reporting module 165 may also be adapted to log and/or store atimestamp indicative of the time and day on which stored data is/wasacquired. The logging/reporting module 165 may be adapted to reportstored data, or any portion thereof, to a patient, a physician, a caregiver, an external computer, a database unit 150, a local database unit155 and/or a remote device 192. In some embodiments, logging/reportingmodule 165 may be part of the MD 100 rather than monitoring unit 170, asdepicted by dotted lines in FIG. 1.

In one embodiment, MD 100 may further include a responsive action unit144 to perform a variety of actions in response to the SCA unit 140 orthe CAC 142. Such actions may include, for example, reporting a changefrom the first classification to the second classification; reportingthe absence of a change from the first classification to the secondclassification; displaying a result of at least one of the firstclassification analysis, the second classification analysis, or anoutput of CAC 142; identifying the emergence of a new class based on theCAC 142; identifying the disappearance of a prior class based on thecomparing; identifying one or more outlier seizure events; identifyingan effect of a therapy; providing a therapy to the patient; identifyinga proposed change in therapy; identifying a proposed additional therapy;and identifying an extreme seizure event.

In one embodiment, responsive action unit 140 may include a therapy unit120 to provide a therapy to the patient. In a particular embodiment, thetherapy may include an electrical stimulation provided by a lead 101 toone or more electrodes 106, 108 coupled to a target tissue such as abrain area, a cranial nerve (e.g., a vagus, trigeminal, hypoglossal, orglosspharyngeal nerve), a spinal cord, a sympathetic nerve or ganglion,or a peripheral nerve. Controller 110 is capable of causing therapy unit120 to provide a therapy in response to one or more of a programmedtherapy, an event detected by seizure determination unit 123, or anoutput of SCA unit 140 or CAC 142. The therapy unit 120 may comprisevarious circuitries, such as electrical signal generators, impedancecontrol circuitry to control the impedance “seen” by the leads, andother circuitry that receives instructions relating to the delivery ofthe electrical signal to tissue.

In addition to a therapy provided by therapy unit 120, controller 110may cause MD 100 to take other responsive actions, and may sendinstructions to monitoring unit 170 to cause the monitoring unit orother units to take further responsive actions to, e.g., an eventdetected by seizure determination unit 123, or an output of SCA unit 140or CAC 142. In one or more embodiments, the responsive action maycomprise providing drug treatments, oxygen treatments, body or braincooling and/or the like. For example, the controller 110 may receivemanual instructions from an operator externally, or may cause anelectrical signal to be generated and delivered based on internalcalculations and programming. In other embodiments, the MD 100 does notcomprise a therapy unit 120, lead assembly 122, or leads 101.

In some embodiments, an event or warning button may 135 may be providedto alert a patient and/or caregiver of the occurrence of an event or anoutput of a seizure classification analysis or comparison. As shown inFIG. 1, the event/warning button 135 may be a separate device from MD100 and MU 170. In other embodiments, event/warning button 135 may beincorporated as part of MD 100 or MU 170.

An embodiment of a medical device adaptable for use in implementing someaspects of embodiments of the present invention is provided in FIG. 2.Details concerning FIG. 2 of the present application is provided in FIG.2 of U.S. application Ser. Nos. 13/040,996 (filed Mar. 4, 2011),13/091,033 (filed Apr. 20, 2011), and 13/333,235 (filed Dec. 21, 2011),and in the discussion thereof. As previously noted, the foregoingapplications are each incorporated by reference herein.

Turning now to FIG. 3, a block diagram depiction of an exemplaryimplementation of the body index determination unit 121 of FIG. 1 isshown. The body index determination unit 121 may include hardware (e.g.,amplifiers, accelerometers), tools for chemical assays, opticalmeasuring tools, a body data memory 350 (which may be independent ofmemory 117 or part of it) for storing and/or buffering data. The bodydata memory 350 may be adapted to store body data for logging orreporting and/or for future body data processing and/or statisticalanalyses. Body index determination unit 121 may also include one or morebody data interfaces 310 for input/output (I/O) communications betweenthe body index determination unit 121 and sensors 112. Body data frommemory 350 and/or interface 310 may be provided to one or more bodyindex calculation unit(s) 355, which may determine one or ore bodyindices.

In the embodiments of FIG. 3, sensors 112 may be provided as any ofvarious body data units/modules (e.g., autonomic data acquisition unit360, neurological data acquisition unit 370, endocrine data acquisitionunit 373, metabolic data acquisition unit 374, tissue stress marker dataacquisition unit 375, and physical fitness/integrity determination unit376) via connection 380. Connection 380 may be a wired connection (e.g.,lead 111 from FIG. 1) a wireless connection, or a combination of thetwo. Connection 380 may be a bus-like implementation or may include anindividual connection (not shown) for all or some of the body dataunits.

In one embodiment, the autonomic data acquisition unit 360 may include acardiac data acquisition unit 361 adapted to acquire a phonocardiogram(PKG), EKG, echocardiography, apexcardiography and/or the like, a bloodpressure acquisition unit 363, a respiration acquisition unit 364, ablood gases acquisition unit 365, and/or the like. In one embodiment,the neurologic data acquisition unit 370 may contain a kinetic unit 366that may comprise an accelerometer unit 367, an inclinometer unit 368,and/or the like; the neurologic data acquisition unit 370 may alsocontain a responsiveness/awareness unit 369 that may be used todetermine a patient's responsiveness to testing/stimuli and/or apatient's awareness of their surroundings. Body index determination unit121 may collect additional data not listed herein, that would becomeapparent to one of skill in the art having the benefit of thisdisclosure.

The body data units ([360-370], [373-377]) may be adapted to collect,acquire, receive/transmit heart beat data, EKG, PKG, echocardiogram,apexcardiogram, blood pressure, respirations, blood gases, bodyacceleration data, body inclination data, EEG/ECoG, quality of lifedata, physical fitness data, and/or the like.

The body data interface(s) 310 may include various amplifier(s) 320, oneor more A/D converters 330 and/or one or more buffers 340 or othermemory (not shown). In one embodiment, the amplifier(s) 320 may beadapted to boost and condition incoming and/or outgoing signal strengthsfor signals such as those to/from any of the body data acquisitionunits/modules (e.g., ([360-370], [373-377])) or signals to/from otherunits/modules of the MD 100. The A/D converter(s) 330 may be adapted toconvert analog input signals from the body data unit(s)/module(s) into adigital signal format for processing by controller 210 (and/or processor215). A converted signal may also be stored in a buffer(s) 340, a bodydata memory 350, or some other memory internal to the MD 100 (e.g.,memory 117, FIG. 1) or external to the MD 100 (e.g., monitoring unit170, local database unit 155, database unit 150, and remote device 192).The buffer(s) 340 may be adapted to buffer and/or store signals receivedor transmitted by the body index determination unit 121.

As an illustrative example, in one embodiment, data related to apatient's respiration may be acquired by respiration unit 364 and sentto MD 100. The body index determination unit 121 may receive therespiration data using body data interface(s) 310. As the data isreceived by the body data interface(s) 310, it may beamplified/conditioned by amplifier(s) 320 and then converted by A/Dconverter(s) into a digital form. The digital signal may be buffered bya buffer(s) 340 before the data signal is transmitted to othercomponents of the body index determination unit 121 (e.g., body datamemory 350) or other components of the MD 100 (e.g., controller 110,processor 115, memory 117, communication unit 160, seizure determinationunit 123, or seizure metric determination unit 125 or its sub-units, orthe like. Body data in analog form may be also used in one or moreembodiments.

Body index determination unit may 121 may use body data from memory 350and/or interface 310 to calculate one or more body indices in body oneor more body index calculation unit(s) 355. A wide variety of bodyindices may be determined, including a variety of autonomic indices suchas heart rate, blood pressure, respiration rate, blood oxygensaturation, neurological indices such as maximum acceleration, patientposition (e.g., standing or sitting), and other indices derived frombody data acquisition units 360, 370, 373, 374, 375, 376, 377, etc.

Turning now to FIG. 4, an MD 100 (as described in FIG. 3) is provided,in accordance with one illustrative embodiment of the present invention.FIG. 4 depicts the body data acquisition units of FIG. 3, as inaccordance with one embodiment, included within the MD 100, rather beingexternally coupled to the MD 100, as shown in FIG. 3. In accordance withvarious embodiments, any number and type of body data acquisition unitsmay be included within the MD 100, as shown in FIG. 4, while other bodydata units may be externally coupled, as shown in FIG. 3. The body dataacquisition units may be coupled to the body index determination unit121 in a fashion similar to that described above with respect to FIG. 3,or in any number of different manners used in coupling intra-medicaldevice modules and units. The manner by which the body data acquisitionunits may be coupled to the body data collection module 275 is notessential to, and does not limit, embodiments of the instant inventionas would be understood by one of skill in the art having the benefit ofthis disclosure. Embodiments of the MD depicted in FIG. 4 may be fullyimplantable or may be adapted to be provided in a system that isexternal to the patient's body.

As shown in FIG. 1, MD 100 in some embodiments may include a seizuremetric determination unit 125 to determine one or more types of seizuremetrics. Various subunits may be provided to determine different seizuretypes of seizure metrics, e.g., seizure severity indices determined bySSIU 126, inter-seizure intervals determined by ISIU 127, patientseizure impact determined by PIU 128, and time of occurrence determinedby TOU 129.

Turning now to FIG. 5A, a block diagram depiction of a seizure severityindex unit (SSIU) 126 is provided, in accordance with one illustrativeembodiment of the present invention. In one embodiment, the SSIU 126 maybe adapted to determine a seizure severity index (SSI). The SSI unit 126may use body data (e.g., from data acquisition units 360, 370, 373, 374,375, 376, 377 shown in FIG. 3), body index data (e.g., from body indexdetermination unit 121) and/or seizure data from determination unit 123.One or more subunits may be provided in SSIU 126 to determine differentkinds of seizure severity indices. Data from various body index and/ordata units may be provided to an SSI value determination unit 530,which, together with information from seizure determination unit 123(e.g., indicating that a seizure has occurred), is used to determine oneor more seizure severity indices.

In one embodiment, data is provided to SSI value determination unit 530from one or more body index units such as neurologic index unit 510,autonomic index unit 520, endocrine index unit 540, metabolic index unit542, tissue stress marker index unit 550, and/or physicalfitness/integrity unit 552, as well as seizure data (e.g., from seizuredetermination unit 123). SSI value determination unit 530 may determinea number of different seizure severity indices based upon theinformation provided, including autonomic indices relating to theseverity of the seizure event, such as the maximum heart rate during theseizure, the time elapsed from detection of the seizure to the maximumheart rate, the rate of change of heart rate from a baseline heart rateat the time of seizure detection until the maximum seizure heart rate,the time required for heart rate to return to baseline (measured from,e.g., the time of seizure detection, the time of maximum HR during theseizure, or another reference time), the rate of change of heart rateduring the return to baseline, etc. In addition to the foregoingcardiac-based seizure severity indices, a variety of neurologic indicesmay be determined from kinetic data such as a triaxial accelerometer orinclinometer, including the maximum acceleration recorded during theseizure, the duration of non-physiologic movements (e.g., pathologicalmovements associated with a convulsive seizure, whether the patient'sposture changed (e.g., from a fall) during the seizure.

The neurologic index unit 510, autonomic index unit 520, endocrine indexunit 540, metabolic index unit 542, stress marker index unit 550 and/orphysical fitness/integrity unit 552 may be adapted to transmit theirrespective index values to an SSI value determination unit 530. The SSIvalue determination unit 530 may determine one or more of a neurologicindex value, an autonomic index value, an endocrine index value, ametabolic index value, a stress marker index value, a physicalfitness/integrity index, seizure data, seizure burden data and/or otherbody data to determine a seizure severity index value (SSI value), asdescribed above with respect to FIG. 1. In one embodiment, the SSI valuemay be indicative of the overall severity of a seizure event and/orextreme seizure event. The SSI value may also be indicative of theintensity, duration and/or spread of a seizure event. The SSI value maybe transmitted/provided to seizure classification analysis unit 140,classification analysis comparator 142, responsive action unit 144,memory 117, monitoring unit 170, and/or remote device 192, among otherunits within the system comprising MD 100.

Turning now to FIG. 5B, a block diagram depiction of a patient seizureimpact unit (PIU) 128 is provided, in accordance with an illustrativeembodiment. PIU 128 may be adapted to determine a patient seizureimpact. The PIU 128 may use body data (e.g., from data acquisition units360, 370, 373, 374, 375, 376), seizure data from seizure determinationunit 123, data input from a patient or caregiver, sensed informationfrom the patient's environment, and/or stored data from memory 117. Oneor more subunits may be provided in PIU to determine different kinds ofpatient impacts associated with the seizure.

Data from various body index and/or data units may be provided to aPatient Seizure Impact value determination unit 531, which, togetherwith information from seizure determination unit 123 and/or input datafrom the patient, a caregiver, or the patient's environment, is used todetermine one or more patient seizure impact values. In one embodiment,data is provided to PSI value determination unit 531 from one or more ofa sensor of the patient's environment, historical data from memory 117,a body index units such as neurologic index unit 510, autonomic indexunit 520, endocrine index unit 540, metabolic index unit 542, tissuestress marker index unit 550, as well as seizure data (e.g., fromseizure determination unit 123) and/or quality of life data from aquality of life determination unit 556.

Patient seizure impact value determination unit 531 may determine anumber of different patient negative impacts, or identify conditions oractivities that may increase the probability of a negative impact shouldthe patient have a seizure while under those conditions or whileperforming those activities. Negative seizure impacts include but arenot limited to periods of cardiac dysfunction (e.g., asystole or otherarrhythmias that do not ordinarily accompany the patient's seizures orchanges in the morphology of the P-QRS-T), changes in the normalrespiratory rhythm (hyperventilationor apnea) or in the respiratorypatterns (e.g., Cheyne-Stokes), extended periods of unresponsivenessbeyond, e.g., the 90th percentile for the patient's historical seizures,cognitive deficits, depression, worsening of quality of life, deviationsof metabolic or endocrine indices from normalcy, increase in theconcentration or number of stress markers. In one embodiment, PIU 128 iscapable of detecting one or more of a broken bone, head trauma and abrain contusion, caused by a fall caused by a seizure, burns, etc. Inanother embodiment, PIU 128 is capable of detecting the presence ofrisks in the patient's surrounding environment (e.g., taking a bath in atub or walking downstairs), or of activities (e.g., swimming, operatingpower equipment) that may lead to injuries, even death, should thepatient have a seizure while engaged in them. The PIU value may betransmitted/provided to one or more of seizure classification analysisunit 140, classification analysis comparator 142, responsive action unit144, memory 117, monitoring unit 170, and/or remote device 192, amongother units within the system comprising MD 100. PSI value determinationunit 531 may further include a seizure burden unit 573, which maydetermine a seizure burden value for a patient based upon cumulativedata determined by PSI value determination unit 531. Seizure burden unitmay provide an indication of the overall burden of the patient'sepilepsy to the patient's life as a result of PSI values determined overlonger time periods such as weeks, months, or years.

Turning now to FIG. 5C, a block diagram depiction of an inter-seizureinterval unit (ISIU) 127 is provided, in accordance with oneillustrative embodiment. ISIU 127 may be adapted to determine aninter-seizure interval index (ISI index). The ISI unit 127 may use datafrom seizure determination unit 123 and/or Time of Occurrence Unit (TOU)129, such as the time of detection of the seizure, the seizure end time,post-ictal start and end times, and similar data for prior seizures(e.g., from memory 117), seizure duration data, time spent in seizuredata, seizure severity index data, patient seizure impact data, time ofoccurrence data, and other data in determining the ISI index. In oneembodiment, body data may include, but is not limited to, neurologicand/or autonomic body data, endocrine data, stress marker data, physicalactivity data, and/or the like. Various data described above, alone orin any combination may be transmitted to an ISI index valuedetermination unit 532. The ISI index value determination unit 532 mayuse a neurologic index value, an autonomic index value, an endocrineindex value, a stress marker index value, a physical fitness indexseizure data, and/or other body data to determine an inter-seizureinterval index value (ISI index value), as described above with respectto FIG. 1.

In one embodiment, the ISI index value may be indicative of the overallseverity of one or more seizure events and/or extreme seizure eventsbased upon one or more intervals of time between events. Decreasing orshortened intervals (compared to reference values) between successiveseizures may indicate a medical emergency for the patient (e.g., statusepilepticus and/or an elevated risk of Sudden Death in Epilepsy, orSUDEP). The ISI index value may also be indicative of the intensity,duration and/or spread of one or more seizure/extreme seizure events hadby a patient. The ISI index value may be transmitted/provided to one ormore of seizure classification analysis unit 140, classificationanalysis comparator 142, responsive action unit 144, memory 117,monitoring unit 170, and/or remote device 192, among other units withinthe system comprising MD 100.

FIG. 5D provides a block diagram depiction of a Time of Occurrence Unit(TOU) 129, in accordance with one illustrative embodiment. TOU 129 maybe adapted to determine a time of occurrence of a seizure. This may inone embodiment constitute determining and logging a simple timestamp fortransmission to a monitoring unit, which then determines, e.g., the date& year, and time of day at which a seizure event occurs. In otherembodiments, the MD 100 itself determines the date, year and time of dayof the seizure event. In addition, the TOU 129 may also provide timeinformation related to the seizure based upon body data, such as the endtime of the seizure, the time from detection to one or more otheroccurrences, such as one or more body indices reaching a particularvalue (e.g., the time between seizure onset and maximum heart rate, thetime between seizure onset and the patient becoming unresponsive, orother events that may provide meaningful information of the seizure byrelating it to time of seizure onset).

TOU 129 may use data from body index determination unit 121, a clock(e.g., a clock in controller 110), seizure determination unit 123, SSI,ISI, or patient seizure impact (PSI) data, and/or one or more of bodyindex determination units 510, 520, 540, 542, 550, etc.). Various datadescribed above, alone or in any combination may be transmitted to atime of occurrence determination unit 533. The TOU determination unit533 may use the foregoing data to determine a time of occurrence valuedescribed above. Time of occurrence values may be transmitted/providedto one or more of classification analysis unit 140, classificationanalysis comparator 142, responsive action unit 144, memory 117,monitoring unit 170, and/or remote device 192, among other units withinthe system comprising MD 100.

Turning now to FIG. 6, a block diagram depiction of an event/warningunit 135 is provided, in accordance with one illustrative embodiment ofthe present invention. In one embodiment, the event/warning unit 135 maybe adapted to provide a warning of a seizure event, a seizureclassification, and/or a result of a comparison of two or more seizureclassification analyses. In various embodiments, seizure events and/orextreme seizure events may include a seizure, a severe seizure, apresent or past state of status epilepticus, an increased risk of astate of status epilepticus, a risk of SUDEP, a result of a seizureclassification analysis, a feature of a seizure class, a result of acomparison of a seizure classification analyses, a change in a seizureclass, or the emergence of a new seizure class or the disappearance of aseizure class. The event/warning unit 135 may provide a warning to apatient, a physician, a care giver, the logging/reporting module 165,the monitoring unit 170, the remote device 192, the memory 117, thedatabase 150/1255, and/or the like.

The event/warning unit 135 may include a warning generating unit 610, inaccordance with one embodiment. The event/warning unit 135 may beadapted to receive extreme epileptic event/state data from extremeepileptic event/state detection, seizure metric data from SMDU 125,seizure class information from seizure classification analysis unit 140,and changes in seizure classes from classification analysis comparator142. In various embodiments, the event/warning unit 135 may be adaptedto receive other signals and/or data in addition to, or alternativelyto, the aforementioned data, as shown in FIG. 6. In one embodiment, thewarning generating unit 610 may take any data received by theevent/warning unit 135 as an input to generate a warning. The warningmay be a general warning related to a seizure or seizure event, and/oran indication or warning of status epilepticus. In one embodiment, theevent/warning unit 135 may include a warning regarding a seizure classor a change in a seizure class from seizure classification analysis unit140 and/or classification analysis comparator 142.

The warning may manifest as a warning tone or light implemented by anearby object adapted to receive the indication of a seizure event fromthe MD 100; an automated email, text message, telephone call, or videomessage sent from the MD 100, either directly or via an monitoring unit170, to an emergency dispatch system, the patient's cellular telephone,PDA, computer, etc. The characteristics of the warning may depend on thetype and severity of an event or of the detected change. The warning mayinclude a time indication (e.g., date and time) of when the warning wasissued, to enable a patient or caregiver to appreciate that thepatient's condition may be deteriorating, improving, or stable and thecorrelations, if any, with circadian or other biological rhythms (e.g.,menses). Warning indications may be logged and significant changes inseverity, type, frequency, or intervals of warnings may, in someembodiments, be used to provide further warnings or alerts. Such awarning may allow the patient or his/her physician/caregivers to takemeasures protective of the patient's well-being and those of others,e.g., pulling out of traffic and turning off a car, when the patient isdriving; stopping the use of machinery, contacting another adult if thepatient is providing childcare, removing the patient from a swimmingpool or bathtub, lying down or sitting if the patient is standing, etc.

Turning now to FIG. 7, a flowchart depiction of a method for determiningseizure metrics, performing seizure classification(s), performingclassification comparison, and/or taking action in response to aclassification or classification comparison is provided, in accordancewith one illustrative embodiment of the present invention. The MD 100acquires and/or receives body data (step 710). The data may be providedby one or more body data acquisition units such as units 360-370 and373-377 (see FIGS. 3, 4), which may include, e.g., electrodes,accelerometers, chemical sensors, thermal sensors, pressure sensors,optical sensors and other structures to sense body signals of thepatient. In one embodiment, the body index determination unit 121receives the body data, which may be indicative of whether or not aseizure or seizure event has occurred or is likely to occur. Afterperforming buffering, amplification, and A/D conversion of the bodydata, body index determination unit may determine one or more bodyindices derived from the data.

The body index/indices determined by BIDU 121 may be stored, e.g., inmemory 117 and/or provided to seizure determination unit 123, which mayuse one or more algorithms to process the body index/indices determinedby BIDU 121 to determine whether or not a seizure event has occurred(720). In some embodiments, all or portions of the body indexdetermination function or unit 121 may be provided as part of seizuredetermination unit 123. Details on using multiple streams of body datato detect seizures are provided in co-pending U.S. application Ser. Nos.12/896,525 filed Oct. 1, 2010, 13/098,262 filed Apr. 29, 2011, and13/288,886 filed Nov. 3, 2011, each incorporated by reference herein. Ifthe MD 100 determines that no seizure or seizure event has occurred, theMD 100 will continue to monitor for body data (step 730) and return tostep 710.

MD 100 may use one or more body indices determined by BIDU 121 to detectthe occurrence of a seizure event. If MD 100 determines (step 720) thata seizure event has occurred or is likely to occur, the method proceedsto determine or acquire an autonomic index (step 740), a neurologicindex (step 750), a tissue stress marker index (step 743), a metabolicindex (step 748) and/or an endocrine index (step 747). In oneembodiment, the autonomic index, the neurologic index, the stress markerindex, the metabolic index and/or the endocrine index/indices areacquired and/or determined using an SSI unit 126 (typically comprisingan autonomic index unit 520, a neurologic index unit 510, an endocrineindex unit 540, metabolic index unit 542 and/or a stress marker indexunit 550). Steps 740, 743, 747-748 and/or 750 may begin at the same timeand end at the same time, or at different times, according to differentembodiments contemplated herein. In other words, steps 740, 743, 747-748and/or 750 may begin and be completed substantially in parallel (i.e.,at approximately the same time or at the same time), in series, orindependently of each other.

The medical device may determine one or more seizure metrics usingseizure metric determination unit (SMDU) 125. Seizure metrics mayinclude using the autonomic index, the neurologic index, the stressmarker index, the metabolic index and/or the endocrine index todetermine an SSI value (step 760); an inter-seizure interval index (ISIindex) value (step 765); a patient impact (PI) value (step 767). Otherseizure metrics may include a time of occurrence of the seizure event(step 763). Typically, the SSI value is determined by SSI unit 126(which may comprise an SSI determination unit (530)). In one or moreembodiments, additional data may also be used to determine the SSIvalue. Typically, the ISI index value is determined by ISI unit 127. Inone or more embodiments, additional data may also be used to determinethe ISI index value. The determination of the SSI value and the ISIindex value may occur in parallel or at different times. In oneembodiment, only one of the values may be determined. For example, inone embodiment, only the SSI value may be calculated while no ISI indexvalue is calculated. In another embodiment, only the ISI index value maybe calculated while no SSI value is calculated.

Once one or more of an SSI value (step 760), an ISI value (step 765), aPI value (step 761, and/or a time of occurrence (step 763) isdetermined, MD 100 may perform one or more seizure classificationanalyses (step 770) to determine one or more seizure classes based onthe seizure metric values determined in steps 760-765. The seizureclassification analysis may be performed by SCA unit 140 (FIG. 1). Afterthe seizure classification analysis, the method may proceed to comparingone or more classification analyses (step 790) to identify changes inone or more seizure classes. The comparison of the classificationanalyses may be performed by the classification analysis comparator 142(FIG. 1). The MD 100 may then take responsive action(s) based upon theseizure classification and/or the classification analysis comparison.Responsive actions may include providing a warning, providing a therapy(e.g., drug/chemical therapy, electric stimulation, cooling, supportivecare, oxygen administration), logging/reporting, and/or the like.

FIG. 8 is a flowchart depicting a method for warning and/or takingaction in response to determining an extreme seizure event. FIG. 8 issubstantially similar to FIG. 8 of co-pending U.S. application Ser. No.13/040,996. A discussion of FIG. 8 herein is provided in the '996application.

FIG. 9 provides a flowchart depicting a method for warning and/orproviding a treatment to a patient in response to a seizure event and/orextreme seizure event, and FIG. 10 is a stylized depiction of the stepof determining a non-automatic treatment plan of steps 967/970 of FIG.9. FIGS. 9 and 10 are substantially similar to FIGS. 9 and 10 ofco-pending U.S. application Ser. No. 13/040,996. A discussion of FIGS. 9and 10 herein is provided in the '996 application.

Turning now to FIG. 11, a flowchart depiction of a method foridentifying changes in an epilepsy patient's disease state or conditionby performing classification analyses is provided, in accordance withone illustrative embodiment of the invention. The medical device 100acquires and/or receives body data at step 1110, typically by body indexdetermination unit 121. The body data may be indicative of whether ornot a seizure or seizure event has occurred, is occurring or is likelyto occur. After performing buffering, amplification and A/D conversionof the body data, the MD 100 determines at least one body index(1111-16). Body index determination unit 121 may determine one or morebody indices for detection of seizure events. The indices may be one ormore of an autonomic index (1111), a neurologic index (1112), ametabolic index (1113), an endocrine index (1114), a tissue index or atissue stress index (1115), a physical fitness index/body integrityindex (1116), or a quality of life index (not shown).

The one or more indices may be used to detect a plurality of seizureevents (1150). In one embodiment, seizures in the plurality of seizureevents may be detected by the seizure determination unit 123 usingdifferent body indices. For example, a first seizure event may bedetected by using an autonomic index such as heart rate, while a secondseizure event may be detected by a body kinetic/accelerometer signal. Ifthe medical device 100 determines that no seizure or seizure event hasoccurred, the medical device 100 will continue to monitor for body datauntil a seizure event is detected.

The MD 100 determines at least one seizure metric for each seizure ofthe plurality of detected seizure events (1160). In one embodiment,seizure metric determination unit (SDMU) 125 determines one or moreseizure metric values for each seizure event. Seizure metrics mayinclude seizure severity indices determined by SSIU 126, inter-seizureintervals determined by ISIU 127, patient seizure impact valuesdetermined by PIU 128, and times of occurrence determined by TOU 129(see FIG. 1). In some embodiments, seizure metrics are determined foreach seizure substantially at the time the seizure is detected. In otherembodiments, seizure metric values may be determined after detection ofa plurality of seizure events. Seizure metric values may in someembodiments be normalized or adjusted based on circadian fluctuationsand other patient or environmental factors.

Returning to FIG. 11, after seizure metrics are determined for aplurality of seizure events, the method also includes performing a firstclassification analysis on the plurality of seizures (1170). The firstclassification analysis may include identifying one or more classes ofseizures. As previously noted, the classification analysis may involvecreating an n-dimensional phase space to classify seizure events. Thephase space may consist of n seizure metrics, and the n seizure metricsmay be selected form a larger m-dimensional matrix of indices that ismaintained for each seizure event.

The method also comprises detecting additional seizure events (1171).This may be accomplished by seizure determination unit 123. Theadditional seizures may be determined in some embodiments using datafrom body index determination unit 121. Seizure metrics for theadditional seizure events are determined (1172) by SMDU 125 (e.g., byone or more of SSIU 126, ISIU 127, PIU 128, and TOU 129).

A second classification analysis is performed (step 1173). This may bedone by seizure classification analysis unit 140 in some embodiments,while separate classification analysis units may be used for each of thefirst and second classification analyses in different embodiments. Inone embodiment, the second classification is made on both the pluralityof seizure events analyzed in the first seizure analysis plus theadditional seizure events. In other embodiments, the secondclassification may include some, but not all, of the plurality ofseizure events in the first classification analysis as well as theadditional seizure events. In still further embodiments, the secondclassification is made only on the additional seizure events.

A comparison of the first and second classification analyses isperformed (1175). In some embodiments, the comparison of the first andsecond seizure analyses may yield information regarding changes in oneor more seizure classes from the first to the second classificationanalysis, the emergence of new classes, or seizures that do not fitwithin any existing classes.

The comparison of the first and second analyses may be used to initiateone or more responsive actions (1180). Illustrative responsive actionsmay include reporting a change from the first classification to thesecond classification (e.g., a seizure class identified in the firstclassification analysis is growing, shrinking, increasing in density,changing shape, shifting its centroid, etc.); reporting the absence of achange from the first classification to the second classification (e.g.,a seizure class identified in the first classification has not changed);displaying a result of at least one of the first classificationanalysis, the second classification analysis, and the comparison;identifying the emergence of a new class in the second classificationanalysis that was not present in the first classification analysis;identifying the disappearance of a class in the second classificationanalysis that was present in the first classification analysis;identifying one or more seizure events that are not part of any class;providing a therapy to a patient; identifying an effect of a therapy;identifying a proposed change in therapy; identifying a proposedadditional therapy; and identifying an extreme seizure event. Anychanges in seizure classifications may then be reported, in oneembodiment, as either deterioration (e.g., appearance of a class ofsevere seizures that has increased the disease burden); improvement(e.g., disappearance of a class of severe seizures with consequentlessening in the disease burden) or stable disease (e.g., no changes inclassification). Other or additional responsive actions—such as warningsif the disease burden is increasing—may be implemented in response to aclassification analysis or a comparison of classification analyses. Inone embodiment, a seizure metric includes whether the seizure detectionwas followed by a responsive action to provide a therapy to the patient.In such embodiments, the classification analysis may include whether theseizure was treated or not, and the effect of the treatment efficacious,no effects, adverse effects). In a particular embodiment theclassification analysis comparison may include comparing treated anduntreated seizures. This may facilitate identification of efficacious orundesirable seizure treatments, including treatments that are effectiveor undesirable for particular classes of seizure events.

The seizure classification analyses performed in methods according toFIG. 11 may be performed in a variety of different mathematical andgraphical ways. In one embodiment, a classification analysis refers to aqualitative, semi-quantitative, or quantitative analysis of one or moreseizure metric values (e.g., SSI values as a function of time withrespect to a threshold) or a statistical analysis (e.g., a histogram)with respect to a number of standard deviations away from an actual orrealized “normal” distribution. Seizures may be classified into one ormore groups according to seizure metric values such as one or moreseizure severity indices (e.g., seizure duration, maximum heart rate,heart rate increase from a reference heart rate, etc.), inter-seizureintervals between a seizure and one or more other seizures, the impacton a patient and/or other bases relevant for classifying seizures (e.g.,type of seizure).

The seizure classification obtainable through this disclosure, mayexpand the known classes (e.g., generalized vs. partial and for partial,simple vs. complex), to include other measurable aspects of seizures ina quantitative manner. For example, in the case of a patient withseizures characterized by:

a) an unprovoked expression of fear for 10 sec. without increase motoractivity but with tachycardia (peak heart rate: 135 beats/min withreference/non-seizure mean heart rate of 82);

b) reversible 2 mm. S-T depression (reference EKG: normal);

c) blood pressure (BP) elevation (BP of 138/89 vs. reference non-seizureBP of 112/70);

d) hyperventilation (peak respiratory rate: 21 breaths/min withreference/non-seizure mean respiratory rate of 10 breaths/min.);

e) loss of awareness as ascertained using a complex reaction time test,(patient failed test 28 sec after first seizure manifestation (nofailure during non-seizure state) and awareness remained impairedcompared to reference/non-seizure reaction time values for 45 min aftertermination abnormal electrical activity);f) motionless late in the course of the seizure (accelerometer registerno motion in the standing position for 65 sec compared to 10 sec forreference/non-seizure upright posture);g) arterial respiratory alkalosis (pH 7.5 vs. mean reference pH of 7.38)and prolactin elevation (30 μg/L with mean reference level of 15 μg/L),this seizure will be classified as partial complex with emotional (fearfor 10 sec), neurologic (loss of awareness for 45 min), hypomotoric (for65 sec), cardiac (43 beat/min increase in rate and ST depression of 2mm), hyperventilation (11 breaths/min increase in rate), arterialalkalosis (pH elevated by 0.5) and endocrine (prolactin elevation of 15μg/L) manifestations. The duration (in sec., min., or hours) of thesechanges may be included in the quantification/classification for addeddetail. The spread of this seizure (which was not treated) may betracked using the temporal evolution of changes in the various indices.The reaction time test failure (28 sec after the first clinicalmanifestation) and motionlessness are indications that the seizurespread from its emergence network (as amygdala and hippocampus) to othernetworks such as the contra-lateral hippocampal formation. An unprovokedexpression of fear and tachycardia in this patient may automaticallytrigger delivery of therapy to either: the commissures (anterior and/orpsalterium) connecting the two mesial temporal networks and/or to theunaffected mesial temporal network to prevent invasion by abnormalactivity.

Classification analyses may be based upon a graphical analysis of, e.g.,an SSI value versus time, or a first SSI value versus a second SSIvalue. More generally, a classification analysis may be performed usingany one or more elements of a matrix to create an n-dimensionalclassification space, within which the seizure events may be located.Results of the classification analysis may be displayed to a user, orused as a basis for taking one or more additional actions such asproviding a warning to a user or caregiver, or providing or modifying atherapy.

For example, in one illustrative embodiment, a patient may have a firstclassification group of seizure events having a relatively shortduration and a relatively low intensity, and a second, smallerclassification group of seizure events having a relatively long durationand a relatively high intensity. Classes and subclasses may beidentified depending upon the closeness or proximity among the metricsof seizure events. If a seizure event classification group (which mayalso be referred to as a seizure class) changes as additional seizuresoccur (e.g., the class grows due to increases in average duration and/oraverage intensity of newly detected seizures or a new class emerges asdictated by the severity of newly detected seizures), this may indicateworsening of the patient's epilepsy along with, for example, increasedrisk of status epilepticus, seizure burden, or sudden death. Similarly,if a seizure class shifts its position, (e.g., inwardly and towards theorigin on a plot of seizure duration versus intensity) causing thedistance (graphically) between two seizure classes to decrease, such ashift/increase may represent an improvement in the patient's epilepsyalong, for example, with a decreased risk of status epilepticus orSUDEP. In an alternate embodiment, the SSI value(s) may be determined bynormalizing a seizure intensity value, a seizure duration value and/or aseizure spread value (or a seizure severity value) to obtain respectivepercentile values. The percentile values may then be averaged todetermine a composite, normalized SSI for a plurality of seizures.

Seizure classification analyses performed by seizure classificationanalysis unit 140 may involve supervised learning algorithms in someembodiments and unsupervised learning algorithms in other embodiments.Embodiments involving supervised learning include pattern matching (suchas a matched filter to which a seizure index pattern is compared) ormany varieties of pattern recognition techniques. Techniques of patternrecognition include using an n-dimension vector of features to identifyclasses; a dot-product or angular filter, identifying categorical (e.g.,extreme) or ordinal (e.g., integer-valued or real-valued) classes.Pattern recognition techniques may be probabilistic or deterministic.Probabilistic techniques may include, e.g., a maximum entropyclassifier, a naïve Bayesian classifier, a support vector machine (SVM),kernel estimation and k-nearest neighbor techniques; decision trees.

In some embodiments, classification analyses may involve clusteringalgorithms. Exemplary clustering algorithms may include categoricalmixture models, K-means clustering, hierarchical clustering (includingagglomerative and divisive methods), kernel principal componentanalyses, regression algorithms. Clustering techniques may besupervised, such as linear regression extensions and artificial neuralanalysis, and Gaussian process regression, or unsupervised. Clustermodels include connectivity or distance-based models focusing on thedistances between seizures in an n-dimensional phase space, andcentroid-based models (K-means models) in which seizure clusters can berepresented as a single median vector of all seizure events. Otherclustering techniques include distribution models (e.g., statisticaldistributions such a multivariate distributions normalized byexpectation maximizing algorithms), density models (e.g., identifyingdense regions in a phase space), and subspace models (e.g.,bi-clustering and co-clustering techniques) are other clusteringtechniques that may be applied to seizure classification in view of thedisclosures herein.

Other techniques include categorical sequence labeling algorithms, bothsupervised and unsupervised, including hidden Markov modeling (HMM),maximum entropy Markov modeling, and conditional Ransom fields.

In some embodiments, real-valued sequence labeling algorithms may beused to classify seizures based on seizure metrics. These algorithms mayinclude the use of Kalman filters and particle filters. Parsingalgorithms, also referred to as predictive tree-structure labels, may beused and may be supervised or unsupervised. One such technique involvesprobabilistic context-free grammar.

In some embodiments, so-called ensemble algorithms may be employed. Inone embodiment, ensemble algorithms may involve supervisedmeta-algorithms for combining multiple learning algorithms together toclassify seizures based on seizure metric data compiled for a pluralityof seizure events. Ensemble algorithms may include bootstrap aggregatingtechniques, boosting techniques, ensemble averaging, mixture-of-experts,and hierarchical mixture-of-experts.

Results of classification analyses may be displayed for a user in avariety of ways. In one embodiment, a seizure classification may involvedisplaying a plurality of seizures graphically using a SSI metric(Y-axis) and time of occurrence (X-axis). Clustering techniques asdescribed above may be used to identify seizure classes, such asidentifying recent, severe seizures or, mild seizures. The SSI metricmay be used to identify a variety of severity-based classes, such asseizures below mean SSI values, within a specified SSI range, elevated,and critically elevated. Time of occurrence (e.g., time of day, seizuresoccurring within the past 1 week, past 1 month, past 1 year, etc.), maybe used to select seizures for classification purposes.

Turning now to FIG. 12, a stylized depiction of a graph indicative ofthe results of an exemplary classification analysis is provided inaccordance with one or more embodiments. In the illustrated embodiment,the y-axis depicts a seizure metric, in this instance seizure durationand the x-axis depicts a seizure metric (e.g., an intensity measure suchas an SSI). Other indices (or more generally, one or more elements in ann-dimensional seizure index matrix) could be used to perform eitherseveral univariate or multi-variate classification analyses inaccordance with various embodiments, by selecting for example the timeof day a seizure occurs, the maximum acceleration recorded by anaccelerometer during the seizure, the time from seizure onset until afall occurs during a seizure, a patient impact (PI) index of a seizure,an inter-seizure interval (ISI), a SSI disclosed herein, or any otherseizure variables. For example, in one embodiment a microphone may beused to record the duration, frequency spectrum and decibel level ofsounds such as the so-called “epileptic cry” associated with a seizure,and track this variable over time to identify changes, if any, in saidseizures and in the class to which they have been assigned. In anotherembodiment, devices to measure noise (in db) or luminance (in candles orlumens) may be used to determine whether a seizure was precipitated bycertain frequency type/level of noise or by certain wavelength(s) oflight or electromagnetic radiation, for the purpose of institutingpreventive steps and/or treatment. Many other quantities may be used ingraphs of the type disclosed generally in FIG. 12, and remain within thespirit and scope of the present invention.

The classification analysis in FIG. 12 is a two-dimensional illustrationof two seizure classes 1281 and 1282 of seizure events 1286 (seizuresare represented by dots). A comparison of the two seizure classes mayresult in one or more measures characterizing the seizures classes,and/or differences between the two seizure classes. For example, in FIG.12, seizure classes 1281 and 1282 may, as additional seizures occur,change to classes 1287 and 1288, respectively. This change indicatesthat classes 1281 and 1282 both are enlarging, changing shape, ormigrating in becoming seizure classes 1287 and 1288 respectively. Thechange in class size (e.g., area or volume, or multidimensionalcapacity) may, in some cases, be indicative of the region in theanalysis in which the seizure events 1285 are distributed.

In one embodiment, the measures (e.g., one or more indices)characterizing each seizure class may in turn be used to define a phasespace, or a seizure class space. In another embodiment, the measures ormetrics used to define a seizure class may be used to compare it toother classes, and to determine one or more differences between theclasses. For example, in FIG. 12, seizure classes 1281 and 1282 may beseparated by a distance L1 (i.e., a distance between the centroids 1290and 1291 respectively, of seizure classes 1281 and 1282). Distance L1may be measured using Euclidian methods (for example, from the centroid1290 of seizure class 1281 to the centroid 1291 of seizure class 1282)or non-Euclidian methods. In alternative embodiments, the distance L1may be measured using different points (i.e., measures other than thecentroid that characterize the seizure class as a whole) of seizureclasses 1281 and 1282. For example, another measure of the differencesbetween seizure classes 1281 and 1282 may be indicated by a distance(not shown) measured by the closest two points of the respective classboundaries), measured using Euclidian and/or non-Euclidian methods.

In one or more embodiments, changes to one or more seizure classes overtime (i.e., as additional seizure events occur and are classified) mayindicate that a patient has a risk/increased risk of an extreme seizureevent/state, or that the patient is having an extreme seizure event. Forexample, an increase in the distance L1 due to an outward shift of theboundary of seizure class 1282 (via any of the arrows 1294(a-c)) mayindicate that seizure class 1282 is becoming more severe overall andthus that the patient's epilepsy is more severe and there is adeterioration in the patient's condition, or that the patient is in anextreme seizure state. Similarly, an outward shift of the boundary ofseizure class 1281 (via any of the arrows 1295(a-c)) may indicate thatseizure class 1281 is becoming more severe overall and thus that thepatient's condition is deteriorating, or that the patient is in anextreme seizure state. This may be the case even though an outward shiftin the boundary of seizure class 1281 (via any of the arrows 1295(a-c))may actually reduce the distance L1 between seizure class 1281 andseizure class 1282. In an alternative embodiment, anincreased/increasing risk of an extreme seizure event/state may existeven though a decrease in overall distance between the seizure classesoccurs. In such a case, the relative distance of seizure classes 1281and 1282 may be measured to determine a risk/increased risk of anextreme seizure event/state, or that the patient is in an extremeseizure state by the following formula:L1=r1+r1′+L2,where r1 is the radius of seizure class 1281 and r1′ is the radius ofseizure class 1282.

Under this formula, if L decreases because of seizure class translation(as shown, e.g., by a shift towards the graph's origin of the centroid1291 within the phase space defining the seizure class 1282), a decreasein seizure severity may be indicated and the patient's condition may beupgraded. However, if L decreases due to seizure class growth (i.e., asize increase in seizure class 1281 as its centroid 1290 moves away fromthe graph's origin), an increase in seizure severity may be indicatedand the patient's condition may be downgraded. It should be noted thatfor more than two seizure classes, radii r1″, r1′″, r1 n′ may bedetermined. Similarly, for non-circular, ellipsoid or non-uniformlyshaped seizure classes, different geometrical indications of class sizeor shape (including major dimensional descriptors, e.g., radius, majoraxes, etc.) may be applied to describe the size and/or shape of aseizure class.

An increase in the seizure class size for either or both of seizureclasses 1281 and 1282 (to classes 1287 and 1288, as shown by arrows L4and L3), may also indicate that the patient's seizures are becoming moresevere (e.g., extreme) and that the patient's condition isdeteriorating. That is, the seizure class 1287 may increase in size to aseizure class spread 1296 and/or seizure class 1288 may increase in sizeto a seizure class spread 1298. Additionally, an increased seizuredensity in seizure class 1281 and/or seizure class 1282 (e.g., thenumber of seizures in the class per unit space in the class) may also beindicative of a risk/increased risk of an extreme seizure event/state,that the patient is in an extreme seizure state or that the patient'scondition is deteriorating. The appearance or emergence of additionalseizure classes (not shown) may be indicative of a risk/increased riskof an extreme seizure event/state, that the patient is in an extremeseizure state, or that the patient's disease state has changed toinclude additional types of seizures not previously recorded. It is alsonoted that inter- and intra-class analyses may be used without departingfrom the spirit and scope of the described embodiments.

In one embodiment, classification analyses may classify seizure eventsinto classes based on groups or clusters of seizures located close toone another in the seizure class phase space. Clustering algorithms maybe applied to seizures to classify them based on intensity orinter-seizure interval; two or more of such measure may be combinedmathematically into a single index, which may be considered as a seizureseverity index. The algorithms may involve a wide variety ofmathematical techniques, including, by way of non-limiting examples,average-linkage clustering, canopy clustering algorithms,complete-linkage clustering, DBSCAN (a density based clusteringalgorithm), expectation-maximization, fuzzy clustering, fuzzy clusteringby local approximation of memberships (FLAME), k-means clustering,k-means++, k-medoids, Linde-Buzo-Gray's, Lloyd's, OPTICS, single-linkageclustering and SUBCLU. One or more algorithms may be applied dependingon the particular case or needs of the patient.

Seizures may be also classified based on time of occurrence in referenceto ultradian or circadian rhythms, such as the sleep-wake cycle, as wellas level and type of cognitive or physical activity among others.

Seizure classification may be also uni-variate in the type of metricused (e.g., SSI or ISI) or multi-variate (e.g., multiple SSIs and/orISIs, SSI and ISI as a function of time of day, time of month, time ofyear, level or type of activity, etc.). In addition, although FIG. 12provides an illustration of a two-dimensional phase space,higher-dimensional phase spaces (e.g., 3 or 4-dimensional phase space upto n-dimensional space) may also be used with suitable adaptation inmathematical techniques used.

Seizure classification may be also performed visually (subjectively orobjectively) using techniques as described in “The Visual Display ofQuantitative Information. E. R. Tufte, 2nd Ed. Graphics Press, Conn.2007 and “Visual Explanation” E. R. Tufte, Graphics Press. Conn. 2005.FIG. 12 illustrates a method of graphically identifying and displayingseizure classification analyses. Based on the present disclosure,similar methods may be used to mathematically determine seizureclassification parameters, which may be displayed or presented to a userin alternative ways.

FIG. 13 is a log-log plot of the probability density function (PDF) ofseizure energy or severity (x-axis); y-axis corresponds to the PDFestimate of the number of seizures. In particular, FIG. 13 is theexemplary PDF of the severity of seizures from an individual patientover a two year period. The thick curve corresponds to the seizuresoccurring between Jan. 1, 2010 and Dec. 31, 2010 and the thin curvethose between Jan. 1, 2011 and Dec. 31, 2011. Visual inspection of thesecurves is all that is required to determine that there is a worsening ofthe patient's epilepsy based on: 1) an increase in the number of moresevere seizures on the thin curve (year 2011 seizures) compared to thethick curve (year 2010 seizures); 2) an overall increase in seizurefrequency, as represented by the general shift upward of the thin curvecompared to the thick curve; and 3) a loss of linearity in the thincurve corresponding to the year 2011, indicating a change in seizuredynamics from a power law distribution (fractal) to a characteristicscale (likely due to an increase or intensification in the couplinglevel among epileptogenic neuronal ensembles).

Turning now to FIG. 14, a log-log plot of an exemplary probabilitydensity function (PDF) of inter-seizure intervals (e.g., the timeelapsed from the onset of a seizure to the onset of the next) in thepatient of FIG. 13 is provided. Inter-seizure interval is plotted on thex-axis and the PDF estimate of the number of seizures on the y-axis. Theinter-seizure intervals for the patient of FIG. 13 over a two yearperiod are compared to determine if changes in seizure classes havetaken place. The thick curve corresponds to the intervals of seizuresoccurring between Jan. 1, 2010 and Dec. 31, 2010 and the thin curvethose between Jan. 1, 2011 and Dec. 31, 2011. Visual inspection of thesecurves is all that is required to determine that changes in seizureclasses have taken place. The number of seizures associated with longinter-seizure intervals in 2011 (thin curve) has decreased compared tothe thick curve (year 2010), while the number of seizures having shorterinter-seizure intervals (i.e., seizures occurring relatively close intime to other seizures) have increased. Viewed in the context of theincreases in seizure frequency and severity shown for the same patientin FIG. 13, the two Figures illustrate a marked increase in diseaseburden and thus to overall deterioration in the patient's condition.

Plots of probability density function estimates may be generated usinghistogram-based estimation methods.

In other embodiments, SSI values may be determined based upon theduration of the seizure event and the peak intensity of the seizureevent. In some such embodiments, the SSI may be calculated as theproduct of the peak intensity of the seizure event and the duration ofthe seizure event. The peak intensity may be the maximum value of anyone, or any number, of body data values during a seizure event. Forexample, in one illustrative embodiment, a patient's heart rate (HR) mayincrease above a pre-determined threshold of 85 beats per minute duringa seizure event. During the seizure event, the patient's HR may reach amaximum value of 135 beats per minute. For a seizure event lasting 30seconds, the peak intensity of the seizure event (i.e., 135) may bemultiplied by the duration (i.e., 30) to obtain an SSI value. In thisexample, an SSI above a pre-determined (or adaptable/adaptive) value mayindicate an increased risk of extreme event (e.g., status epilepticus).Similarly, an ISI value below a pre-determined (or adaptable/adaptive)percentile based upon historical patient data may indicate a risk of anextreme event. For example, if a given SSI value for a patient is abovethe ninetieth percentile (or an ISI value is below the tenth percentile)of the patient's past SSI (or ISI) values, the patient may be at anincreased risk of having an extreme event.

Indexing seizure events may be used in various embodiments to labeland/or classify seizure events and their corresponding severity. Forexample, in one or more illustrative embodiments, a seizure event (“sz”)may be represented, as a function of intensity, duration and seizurespread:sz=f(intensity,duration,spread).

If a substitution is made as: x=intensity, y=duration, and z=spread,then:sz=f(x,y,z).

An indexed overall seizure metric (OSM) may be determined to be thevalue sz=f(x, y, z). It should be appreciated that x, y and/or z may beset as zero for various determinations of sz or left out altogether.That is, sz may be a function of:

(i) one of x, y or z: OSM_(1x),OSM_(1y),OSM_(1z),

(ii) a function of any two of x, y or z: OSM_(2xy), OSM_(2xz),OSM_(2yz), or

(iii) a function of all three (intensity and duration and spread):OSM_(3xyz).

In alternate embodiments, an indexed seizure metric (ISM) may bedetermined as the function of inter-seizure interval, SSI, PI, or TOO.In other embodiments, the indexed seizure metric may be based on two ormore of ISI, SSI, PI, or TOO.

In further embodiments, an indexed systemic patient condition (SPC) maybe determined as the function of patient impact (PI), either alone or incombination with one or more other factors listed herein. Examples ofindexed systemic patient condition described above are not exclusive. Itis contemplated that any combination of the above described indexedseizure severities may be used in accordance with the embodimentsherein. Similarly, a seizure event “sz” may be a function of anycombination of body data (e.g., body data, as described above withrespect to FIG. 1). When referring to the possible SPC combinations, theterm SPCn may be used.

Similarly, a seizure event time series may be used to label and/orclassify multiple seizure events based on their intensity (Si), duration(Sd), extent of spread (Sc) separately or conflated into a seizureseverity index (SSI), inter-seizure interval ISI), patient impact (PI),date and time of occurrence (TOO), response to therapy (Tx), level ofconsciousness (LOC), cognitive (LCA), physical activity (LPA), bodyposition (e.g., upright) (ByP), physical fitness level (PFL) or, qualityof life (QOL). Such a time series may be represented as:

Sztime series=f (Si, Sd, Sc, (or SSI), ISI, PI, TOO, Tx, LOC, LCA, LPA,ByP, PFL, QOL), of each seizure included in the time series. An indexedsystemic patient condition (iSPC) based on a time series of seizureevents (sztime series) may be determined, in some embodiments, as:SPCf(sztime series)

It is noted that the presence or absence of change inclasses/clusters/patterns, including disappearance or emergence ofclasses/clusters/patterns (or parts thereof), is determined to a certainextent by the type of analysis used (e.g., soft vs. hard clusteringrules) and by the size/quality (statistically representative or not) ofthe sample. Therefore, clinical correlation is indicated to betterinterpret the changes if any in the results of the analyses.

A useful way to address the potential ambiguity of classificationresults (i.e., whether a particular seizure is or is not within aparticular seizure class) is to perform a multivariate classificationanalysis (e.g., instead of having only one seizure metric based uponECoG indices, also using one or more additional seizure metrics based,for example, upon kinetic and/or cognitive indices), and to correlatethe results with quantitative clinical indices or metrics. For example,if a change in a seizure class is not identified based on seizureseverity indices (SSI values) derived from the ECoG, by all (or themajority) of a plurality of seizure class analysis measures, or if thereis a non-statistically-significant difference, SSI measured usingcomplex reaction time (a cognitive index) may be used to determine ifthe lack of agreement or of statistical significance is associated withor has an clinical impact which may be beneficial or deleterious. Inthis example, a comparison of complex reaction times associated withseizure in the original class and those in the indeterminateclassification will be made, with two possible results: if there is nodifference in complex reaction time between the original and theunclassified seizures, from a cognitive perspective and clinical impact,the indeterminate class of seizures do not constitute a new class ofseizures. On the other hand, if there is a difference in the complexreaction time, the unclassified seizures may be treated as a differentor new seizure class.

The particular embodiments disclosed above are illustrative only as theinvention may be modified and practiced in different but equivalentmanners apparent to those skilled in the art having the benefit of theteachings herein. Furthermore, no limitations are intended to thedetails of construction or design herein shown other than as describedin the claims below. It is, therefore, evident that the particularembodiments disclosed above may be altered or modified and all suchvariations are considered within the scope and spirit of the invention.Accordingly, the protection sought herein is as set forth in the claimsbelow.

What is claimed is:
 1. A method for identifying changes in an epilepsypatient's disease state, comprising: receiving at least one body datastream; determining at least one of an autonomic index, a neurologicindex, a metabolic index, an endocrine index, a tissue index, or atissue stress index, a physical fitness or body integrity index basedupon the at least one body data stream; detecting a plurality of seizureevents based upon the at least one determined index; determining atleast one seizure metric value for each seizure event in the pluralityof seizure events; performing a first classification analysis of theplurality of seizure events based on the at least one seizure metricvalue for each seizure event; detecting at least one additional seizureevent based upon the at least one determined index; determining at leastone seizure metric value for each of the at least one additional seizureevents, performing a second classification analysis of the plurality ofseizure events and the at least one additional seizure event based uponthe at least one seizure metric value; comparing the results of thefirst classification analysis and the second classification analysis;and performing a further action selected from: a. reporting a changefrom the first classification to the second classification; b. reportingthe absence of a change from the first classification to the secondclassification; c. displaying a result of at least one of the firstclassification analysis, the second classification analysis, and thecomparing; d. identifying the emergence of a new class based on thecomparing; e. identifying the disappearance of a prior class based onthe comparing; f. identifying one or more outlier seizure events notpart of any class; g. identifying an effect of a therapy; h. providing atherapy to the patient in response to the comparing; i. identifying aproposed change in therapy; j. identifying a proposed additionaltherapy; k. identifying an extreme seizure event; l. issuing a warningif a new seizure class appears or an extreme event occurs; m. logging tomemory the time, date and type of change in the patient's seizures. 2.The method of claim 1, wherein performing a first classificationanalysis comprises identifying one or more seizure classes bydetermining one or more relationships among at least a portion of theplurality of seizure events, wherein the one or more relationships arebased on the at least one seizure metric value for each seizure event;wherein performing a second classification analysis comprisesidentifying one or more seizure classes by determining one or morerelationships among at least a portion of the plurality of seizureevents and the at least one additional seizure event, wherein the one ormore relationships are based on the at least one seizure metric valuefor each seizure event; and wherein comparing the results of the firstclassification analysis and the second classification analysis comprisesidentifying a change in at least one class of said one or more seizureclasses from said first classification analysis to said secondclassification analysis.
 3. The method of claim 2, wherein each of theone or more seizure classes identified in the first classificationanalysis and the second classification analysis comprises a seizurecluster based upon the at least one seizure metric, and identifying achange in at least one class of said one or more seizure classescomprises identifying a difference between a seizure class identified inthe first classification analysis and a seizure class identified in thesecond classification analysis.
 4. The method of claim 3 wherein classesare identified by at least one mathematical analysis operation selectedfrom a statistical analysis, a graphical analysis, an unsupervisedmachine learning analysis, a supervised machine learning analysis, and asemi-supervised machine learning analysis.
 5. The method of claim 4wherein the statistical analysis comprises one or more of identifying ameasure of central tendency of the class based on the at least oneseizure metric, determining one or more percentile values based on theat least one seizure metric, determining one or more distributions basedon the at least one seizure metric.
 6. The method of claim 4, furthercomprising the step of receiving training set data for a plurality ofseizures comprising a seizure class wherein the unsupervised machinelearning analysis comprises a clustering analysis selected from acategorical mixture modeling analysis, a K-means clustering analysis, anagglomerative hierarchical clustering analysis, a divisive hierarchicalclustering analysis, a principal component analysis, a regressionalgorithm, an independent component analysis, a categorical sequencelabeling algorithm, and an unsupervised Hidden Markov Model sequence. 7.The method of claim 4, wherein classes are identified by more than onemathematical analysis operation.
 8. The method of claim 1, whereindetermining at least one seizure metric comprises determining at leasttwo seizure metric values; wherein performing a first classificationanalysis comprises identifying one or more seizure classes bydetermining one or more relationships among at least a portion of theplurality of seizure events, wherein the one or more relationships arebased on the at least two seizure metric values for each seizure event;wherein performing a second classification analysis comprisesidentifying one or more seizure classes by determining one or morerelationships among at least a portion of the plurality of seizureevents and the at least one additional seizure event, wherein the one ormore relationships are based on the at least two seizure metric valuesfor each seizure event; and wherein comparing the results of the firstclassification analysis and the second classification analysis comprisesidentifying a change in at least one seizure class of said one or moreseizure classes from said first classification analysis to said secondclassification analysis.
 9. The method of claim 2, wherein the change inat least one class in moving from said first classification analysis tosaid second classification analysis is at least one of: a shift in acentroid of said class; a change in area defined by said class; a changein a shape defined by said class; a change in density characterizingsaid class.
 10. The method of claim 2, further comprising identifying achange in a relationship between a first class and a second class ofseizures in moving from the first classification analysis to the secondclassification analysis.
 11. The method of claim 10, wherein the changein the relationship comprises at least one of: a change in the distancebetween centroids of the first and second classes; and a change indistance between the two closest points of the first and second classes.12. The method of claim 1, wherein the at least one seizure metric valuecomprises at least one of: a seizure severity index, an inter-seizureinterval, a seizure frequency per unit time, a seizure duration, apost-ictal energy level, a patient posture at the time of the seizure, apatient wake state at the time of the seizure, a time of day at whichthe seizure occurs, a level of responsiveness associated with theseizure, a level of cognitive awareness associated with the seizure, apatient fitness level at the time of the seizure, a patient seizureimpact, and a rate of change of one of the foregoing over at least oneof a microscopic, mesoscopic or macroscopic time scale.
 13. A methodcomprising: identifying at least three initial seizure events in apatient; classifying each initial seizure event into at least a firstclass; identifying at least one additional seizure event; re-classifyingthe first class based upon at least one of the initial seizure eventsand the at least one additional seizure event; and performing aresponsive action based upon the re-classifying.
 14. A non-transitorycomputer readable program storage unit encoded with instructions that,when executed by a computer, perform a method comprising: detecting aplurality of seizure events based upon body data of the patient;determining, for each seizure event, at least one seizure metric valuecharacterizing the seizure event, wherein each of said at least oneseizure metric values comprises one of an autonomic index, a neurologicindex, a metabolic index, an endocrine index, a tissue index, or atissue stress index; performing a first classification analysis of afirst portion of the plurality of seizure events, the classificationanalysis comprising assigning each seizure event in the first portion toat least one seizure class based upon the proximity of the seizuremetric values to each other; performing a second classification analysisof a second portion of the plurality of seizure events, theclassification analysis comprising assigning each seizure event in thesecond portion to at least one seizure class based upon the proximity ofthe seizure metric values, wherein said second portion comprises atleast one seizure event not present in the first portion; comparing theresults of the first classification analysis and the secondclassification analysis; and performing a further action selected from:a. reporting a change from the first classification to the secondclassification; b. reporting the absence of a change from the firstclassification to the second classification; c. displaying a result ofat least one of the first classification analysis, the secondclassification analysis, and the comparing; d. identifying the emergenceof a new class based on the comparing; e. identifying the disappearanceof a prior class based on the comparing; f. identifying one or moreoutlier seizure events not part of any class; g. identifying an effectof a therapy; h. providing a therapy to the patient in response to thecomparing; i. identifying a proposed change in therapy; j. identifying aproposed additional therapy; k. identifying an extreme seizure event. l.identifying a worsening trend in the patient's seizures; m. identifyingan improvement trend in the patient's seizures; n. downgrading thepatient's condition in response to a worsening in the patient'sseizures; and o. upgrading the patient's condition in response to animprovement in the patient's seizures.
 15. The non-transitory computerreadable program storage unit of claim 14, wherein said performing afurther action comprises displaying at least one of a graphicaldepiction or a numerical representation of at least one of the firstclassification analysis, the second classification analysis, and thecomparing.
 16. The non-transitory computer readable program storage unitof claim 14, wherein performing a first classification analysiscomprises identifying one or more seizure classes by determining one ormore relationships among at least a portion of the plurality of seizureevents, wherein the one or more relationships are based on the at leastone seizure metric value for each seizure event; wherein performing asecond classification analysis comprises identifying one or more seizureclasses by determining one or more relationships among at least aportion of the plurality of seizure events and the at least oneadditional seizure event, wherein the one or more relationships arebased on the at least one seizure metric value for each seizure event;and wherein comparing the results of the first classification analysisand the second classification analysis comprises identifying a change inat least one class of said one or more seizure classes from said firstclassification analysis to said second classification analysis.
 17. Thenon-transitory computer readable program storage unit of claim 14,wherein determining at least one seizure metric comprises determining atleast two seizure metric values; wherein performing a firstclassification analysis comprises identifying one or more seizureclasses by determining one or more relationships among at least aportion of the plurality of seizure events, wherein the one or morerelationships are based on at least two seizure metric values for eachseizure event; wherein performing a second classification analysiscomprises identifying one or more seizure classes by determining one ormore relationships among at least a portion of the plurality of seizureevents and the at least one additional seizure event, wherein the one ormore relationships are based on at least two seizure metric values foreach seizure event; and wherein comparing the results of the firstclassification analysis and the second classification analysis comprisesidentifying a change in at least one seizure class of said one or moreseizure classes from said first classification analysis to said secondclassification analysis.
 18. The non-transitory computer readableprogram storage unit of claim 14, further comprising identifying achange in a relationship between a first class and a second class ofseizures in moving from the first classification analysis to the secondclassification analysis.
 19. The non-transitory computer readableprogram storage unit of claim 14, wherein the at least one seizuremetric value comprises at least one of: a seizure severity index, aninter-seizure interval, a seizure frequency per unit time, a seizureduration, a post-ictal energy level, a patient posture at the time ofthe seizure, a patient wake state at the time of the seizure, a time ofday at which the seizure occurs, a level of responsiveness associatedwith the seizure, a level of cognitive awareness associated with theseizure, a patient fitness level at the time of the seizure, a patientseizure impact, and a rate of change of one of the foregoing over atleast one of a microscopic, mesoscopic or macroscopic time scale.
 20. Anon-transitory computer readable program storage unit encoded withinstructions that, when executed by a computer, perform a methodcomprising: detecting a plurality of seizure events in a first timeperiod, wherein each of the seizure events is detected based upon bodydata of the patient; determining at least one seizure metric value foreach seizure event of the plurality of seizure events; performing afirst classification analysis of a first portion of the plurality ofseizure events, wherein the detection of each seizure in the firstportion occurred within a second time period within said first timeperiod, wherein said first classification analysis comprises identifyingat least a first seizure class and a second seizure class based on theat least one seizure metric value, wherein the second seizure classcomprises seizures that are more severe than seizures in the firstseizure class; performing a second classification analysis of a secondportion of the plurality of seizure events, wherein the detection ofeach seizure in the second portion occurred within a third time period,wherein said third time period is a period within said first time periodand wherein at least a portion of said third time period is not withinthe second time period, wherein said second classification analysiscomprises determining, for each seizure event in said third time period,whether the seizure event is within the first seizure class and withinthe second seizure class, based on the at least one seizure metricvalue; identifying at least one of a change in the first seizure classand the second seizure class between the first classification analysisand the second classification analysis; and performing a responsiveaction based on the identifying.